{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "e95cd34d",
   "metadata": {},
   "outputs": [],
   "source": [
    "#!/usr/bin/env python\n",
    "# coding: utf-8\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import accuracy_score, matthews_corrcoef, classification_report, confusion_matrix, roc_curve, auc\n",
    "\n",
    "# Import all classification models\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "np.random.seed(42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "135cd2f8",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_excel(\"Features_dihedrals.xlsx\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "28f7b930",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "         TYPE GROUP    A:26-psi   A:26-phi   A:27-psi   A:27-phi   A:28-psi  \\\n",
      "0          WT     R  141.171825 -71.780293 -42.274790 -53.992619 -26.423655   \n",
      "1          WT     R  133.918363 -69.590337 -30.422553 -66.972577 -12.065461   \n",
      "2          WT     R  127.941929 -61.120568 -12.107870 -70.708515 -14.554588   \n",
      "3          WT     R  140.142303 -82.874125 -14.545695 -72.266886 -31.239808   \n",
      "4          WT     R  130.813072 -81.469950 -26.146504 -68.468895 -33.165265   \n",
      "...       ...   ...         ...        ...        ...        ...        ...   \n",
      "179995  V262A     S  127.625038 -73.450592 -29.194439 -59.613475 -42.901914   \n",
      "179996  V262A     S  135.033131 -63.285651 -34.883503 -60.367067 -26.800236   \n",
      "179997  V262A     S  128.029603 -81.349431 -47.589527 -44.628800 -28.189770   \n",
      "179998  V262A     S  131.569052 -84.354718 -35.928850 -53.411061 -37.408241   \n",
      "179999  V262A     S  137.119512 -93.523630 -46.804478 -47.413303 -23.159267   \n",
      "\n",
      "         A:28-phi   A:29-psi   A:29-phi  ...  A:286-psi  A:286-phi  A:287-psi  \\\n",
      "0      -66.303277 -37.268369 -87.027127  ... -40.942648 -80.609882 -49.145654   \n",
      "1      -69.022995 -46.212207 -89.894134  ... -51.230278 -43.283310 -42.215901   \n",
      "2      -91.662457 -30.138653 -81.249940  ... -54.607126 -46.709129 -31.521234   \n",
      "3      -78.514610 -43.100636 -79.176567  ... -34.233795 -67.100572 -35.463009   \n",
      "4      -68.426481 -49.230363 -73.921142  ... -36.057006 -67.433284 -36.910439   \n",
      "...           ...        ...        ...  ...        ...        ...        ...   \n",
      "179995 -78.859516 -34.331259 -66.521916  ... -40.117638 -54.228731 -45.304236   \n",
      "179996 -76.316071 -44.046094 -71.946844  ... -56.484932 -47.573615 -26.565757   \n",
      "179997 -76.135363 -33.846174 -77.135057  ... -51.233260 -70.607510 -38.187945   \n",
      "179998 -71.515815 -36.927989 -75.017467  ... -37.434208 -60.516242 -52.289450   \n",
      "179999 -80.995354 -37.272268 -74.997354  ... -42.072584 -60.539949 -36.620823   \n",
      "\n",
      "        A:287-phi  A:288-psi  A:288-phi  A:289-psi  A:289-phi  A:290-psi  \\\n",
      "0      -64.972856 -48.906824 -61.748768 -26.891682 -68.886184 -56.821078   \n",
      "1      -75.848954 -42.071103 -62.894582 -28.367213 -70.818011 -40.275199   \n",
      "2      -62.514936 -46.490578 -80.161416 -17.822411 -76.514344  -9.260813   \n",
      "3      -84.544144 -55.703470 -57.833845 -29.527320 -66.222539 -35.429191   \n",
      "4      -79.613162 -56.470940 -60.401171 -50.955238 -62.453717 -36.404179   \n",
      "...           ...        ...        ...        ...        ...        ...   \n",
      "179995 -84.066716 -56.768472 -50.399302  -2.741458 -75.363533 -57.135987   \n",
      "179996 -76.789406 -34.948504 -56.877699  -1.409235 -92.257589 -41.150623   \n",
      "179997 -67.610909 -46.431417 -48.062175 -21.252003 -81.217431 -58.715423   \n",
      "179998 -65.129825 -37.418159 -53.036566  -2.794320 -83.140482 -28.670993   \n",
      "179999 -73.806380 -57.019628 -53.882482 -25.441403 -70.962429 -50.163052   \n",
      "\n",
      "         A:290-phi  \n",
      "0       -75.952840  \n",
      "1       -81.229772  \n",
      "2       -79.562657  \n",
      "3       -84.173494  \n",
      "4       -59.769342  \n",
      "...            ...  \n",
      "179995 -106.747918  \n",
      "179996 -106.042583  \n",
      "179997  -86.533687  \n",
      "179998 -120.964837  \n",
      "179999  -81.817825  \n",
      "\n",
      "[180000 rows x 528 columns]\n"
     ]
    }
   ],
   "source": [
    "print(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "a1f53bf1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Encode 'S' (Susceptible) as 0 and 'R' (Resistant) as 1\n",
    "data['GROUP'] = data['GROUP'].map({'S': 0, 'R': 1})\n",
    "    \n",
    "# Separate features (X) and target (y)\n",
    "X = data.drop(['GROUP', 'TYPE'], axis=1)\n",
    "y = data['GROUP']\n",
    "    \n",
    "# Normalize features to a 0-1 range\n",
    "scaler = MinMaxScaler()\n",
    "X_scaled = pd.DataFrame(scaler.fit_transform(X), columns=X.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "872401a2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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s7KzT18779yc8PFyn/oIFCwQAQkJCgiAIgnD9+nVBIpEIr7/+er7PSE9PFzw9PYXu3bvrlKtUKqFhw4ZCs2bN8r2W6HmcvkdkYevXr8c///yj8zp27JhOHYVCga1btyI5ORmNGzeGIAj47rvvtKOpntWnTx84ODho37u6uqJ79+74+++/C9z54969exg9ejT8/f0hlUohk8kQEBAAALhw4UKhn8PX1xfNmjXTKWvQoIHONya//vor6tWrhxdffBG5ubnaV1hYmM5Q4z///BMAtN/+5XnttdcKjSPP8OHDIRaLtXP1Ac23is7OzhgwYIC2bN++fXjllVfg7u4OiUQCmUyG6dOnIzk5Gffu3dP7PC+88IJRz9+2bRtatWoFFxcXbXuuWrXKYFu2b98erq6u2vc+Pj6oUKGCtu0yMjKwf/9+9O/fv8D1vH799Ve0b98eFStW1GnfvBFmxuxWV7duXb2Fw1977TWkpqbi5MmTADQLoXbr1g1RUVHaYfSbNm1CcnIy/ve//xX6DEAzqkomk0Eul6N27do4fPgwZs2ahfDwcKOuJyIiys/zO8nWrl0bALRrFj5b/vwUpuK4fv06XnvtNfj6+mr7FG3btgVgXF/KkM6dO8PX11dnpFPeCJtnRyabow9QrVo1vT7pP//8ozd9sFWrVpg7dy4WL16MMWPG4I033sCIESMM3vP5vlz//v0hlUq1fb38mNI/M6Rr1646/eQGDRoAgPbnffXqVVy8eFEb37Nt1qVLFyQkJODSpUsANP3SDh06wMfHR3s/iUSi058siEgkwvDhw3H27FmcOHECAJCcnIwdO3bg1Vdf1S5sn5ubi3nz5qFOnTqQy+WQSqWQy+W4cuWKwd+fV1991ajnp6Wl4YMPPkD16tUhlUohlUrh4uKC9PR0g/d9duosoN920dHRUKlUeOedd/J95uHDh5GSkoKhQ4fqtK1arUanTp3wzz//6C3dQZQfTt8jsrDatWsbtdB59erV0bp1a+zcuRNjxozJd/c7X19fg2U5OTlIS0vTmW6VR61WIzQ0FHfu3MG0adNQv359ODs7Q61Wo0WLFsjMzCw0vucXFAc0ybRnr7179y6uXr2a73DtvPWrkpOTIZVK9e5p6LPlJyAgAB06dMCmTZvw6aef4vHjx/j111/x2muvaRNAMTExCA0NRbt27bBixQrtGgw///wz5s6dq/e5jd1x8Mcff0T//v3Rr18/TJ48Gb6+vpBKpVi6dKlOkixPYW334MEDqFQqVK5cucDn3r17Fzt27Ci0fQuS3+8PAJ21EN577z106NAB0dHRCA0NxZIlSxASEoLGjRsX+gxA0ymdPHkyRCIRXF1dUa1aNZ3OY95w/bi4OL3h6kREZL+8vb3h5OSEuLg4iz3D09NT533ermaGyrOysszyzLS0NLRu3RoODg6YM2cOXnjhBTg5OeHmzZvo06ePUX0pQ6RSKQYPHoyvvvoKDx8+RLly5bB27Vr4+fkhLCxMW88cfYC8tUiN8frrr2PatGnIzs7G5MmT8633fL8ir3/3/PpKzzK1f2bI832rvLXL8q69e/cuAGDSpEmYNGmSwXs82y8tqH9kjOHDh+Pjjz/GmjVr0KRJE2zcuBE5OTk6ybwJEyZgyZIl+OCDD9C2bVt4eHhALBZj5MiRBj+zsf3S1157DX/88QemTZuGpk2bws3NDSKRCF26dDF438LaLm9NsIL6pXnt27dv33zrpKSk5LsUBtGzmJQiKiFWrlyJnTt3olmzZvj6668xYMAANG/eXK+eobnkiYmJkMvlcHFxMXjvc+fO4cyZM1i7di2GDh2qLb969ar5PgA0HVFHR0eDiZm884Dmj2Fubq7e7nmGPltBRowYgejoaPzyyy+4c+eO3h//zZs3QyaT4ddff9UZXfbzzz8bvJ9IJDLqud9++y2CgoKwZcsWnWueXZDcFJ6enpBIJLh161aB9by9vdGgQQPMnTvX4PmKFSsW+qz8fn8A3U7Kyy+/jHr16uHrr7+Gi4sLTp48iW+//bbQ++cpX758gR3fsLAwTJ06FT///DM6depk9H2JiKhkk0gk6NChA3777TfcunWr0C9cAM3/FBv6G1pQYqMo8voCzz/LmITOvn37cOfOHfz111/a0VEA8PDhw2LHNXz4cCxcuFC73tH27dsxbtw4nS9zzNEHMJZKpcLrr78ODw8PKBQKjBgxAocOHdIm/56VmJiISpUqad8b6t89z9T+WVHk9TkjIiLQp08fg3XyvhTz8vIqsH9kjMqVKyM0NBSbNm3CZ599hjVr1qB69epo06aNtk7eGkzz5s3TuTYpKQnlypXTu6cx/dJHjx7h119/xYwZMzBlyhRted76p0WRN2r/1q1b+a4Ll9e+X331Vb67AT478oyoIJy+R1QC/Pvvvxg7diyGDBmCAwcOoEGDBhgwYAAePHigV/fHH3/U+dbv8ePH2LFjB1q3bm1wuh/w9I/a8zvgLFu2zIyfQjOc/tq1a/Dy8kJwcLDeKzAwEIBmOhsAbNy4Ued6YxeUzNOrVy94eXlh9erVWLNmDV544QW89NJL2vMikQhSqVSnXTIzM7Fhw4YifsKn95XL5TqdhcTERIO77xnD0dERbdu2xbZt2wrsGHfr1g3nzp1DtWrVDLavMR3S8+fP48yZMzplmzZtgqurq94oqLFjx2Lnzp2IiIiAj48P+vXrV6TPZ0jjxo3RuXNnrFq1SmdhzWcdP35cZ/cfIiKyDxERERAEAW+99RZycnL0ziuVSuzYsUP7PjAwEGfPntWps2/fPqSlpZk1rrx+yPPP2r59e6HXmtKXen7kSWFq166N5s2bY82aNdi0aROys7MxfPhwnTrm6AMYa8aMGThw4AA2btyILVu24MyZM/mOlnq+L7d161bk5ubq7Jj3PEv1z55Vs2ZN1KhRA2fOnDHYXsHBwdqR9e3bt8cff/yhHf0DaBJzW7ZsMemZI0aMwIMHDzB9+nScPn0aw4cP1+krikQivd+fnTt34vbt20X+nHkbCD1/35UrVxa4rEdBQkNDIZFIsHTp0nzrtGrVCuXKlUNsbGy+7WsoiUlkCEdKEVnYuXPnDO50Uq1aNZQvXx7p6eno378/goKCtLubbd26FY0bN8bw4cP1vjWSSCTo2LEjJkyYALVajfnz5yM1NRUzZ87MN4ZatWqhWrVqmDJlCgRBgKenJ3bs2IHo6GizftZx48bhhx9+QJs2bTB+/Hg0aNAAarUa8fHx2LNnDyZOnIjmzZsjNDQUbdq0wfvvv4/09HQEBwfj0KFDJndGFAoFXn/9dXz11VcQBAGffPKJzvmuXbti0aJFeO211/D2228jOTkZn376qcHtqU3RrVs3/PjjjwgPD0ffvn1x8+ZNzJ49G35+frhy5UqR7pm3E2Lz5s0xZcoUVK9eHXfv3sX27duxbNkyuLq6YtasWYiOjkbLli0xduxY1KxZE1lZWbhx4wZ27dqFb775ptBvpCtWrIgePXrg448/hp+fH7799ltER0dj/vz5cHJy0qn7xhtvICIiAn///Tc++ugjs3cu1q9fj06dOqFz585488030blzZ3h4eCAhIQE7duzAd999hxMnTmin+hERkX0ICQnB0qVLER4ejiZNmmDMmDGoW7culEolTp06heXLl6NevXro3r07AGDw4MGYNm0apk+fjrZt2yI2NhZff/21wSUJisPX1xevvPIKIiMj4eHhgYCAAPzxxx/48ccfC722ZcuW8PDwwOjRozFjxgzIZDJs3LhR74seAKhfvz4AYP78+ejcuTMkEgkaNGhQ4N/RN998E6NGjcKdO3fQsmVLvant5ugDZGZm4ujRowbP5Y12iY6ORmRkJKZNm6bdPS0yMhKTJk1Cu3bt0Lt3b53rfvzxR0ilUnTs2FG7+17Dhg3Rv3//fOOwVP/secuWLUPnzp0RFhaGYcOGoVKlSkhJScGFCxdw8uRJbNu2DQDw0UcfYfv27Xj55Zcxffp0ODk5YcmSJSavidSjRw94e3tj4cKFkEgkOrMTAE3/ce3atahVqxYaNGiAEydOYOHChUaNJsyPm5sb2rRpg4ULF8Lb2xuBgYHYv38/Vq1aZXD0lTECAwMxdepUzJ49G5mZmRg0aBDc3d0RGxuLpKQkzJw5Ey4uLvjqq68wdOhQpKSkoG/fvqhQoQLu37+PM2fO4P79+wUmtYh02HKVdaLSrKDd9/DMDnFvvPGG4OTkpLdryrZt2wQA2h3Q8nYbmT9/vjBz5kyhcuXKglwuFxo1aiTs3r3b4LOf3S0kNjZW6Nixo+Dq6ip4eHgI/fr1E+Lj4/V2vMlv9726devqfcahQ4cKAQEBOmVpaWnCRx99JNSsWVOQy+WCu7u7UL9+fWH8+PE6u5o8fPhQePPNN4Vy5coJTk5OQseOHYWLFy8avftenjNnzmh3NLxz547e+dWrVws1a9YUFAqFULVqVSEyMlJYtWqV3mcMCAgQunbtavAZhnbf++STT4TAwEBBoVAItWvXFlasWGFwpx4AwjvvvGPUPWNjY4V+/foJXl5eglwuF6pUqSIMGzZMyMrK0ta5f/++MHbsWCEoKEiQyWSCp6en0KRJE+HDDz8U0tLSCmyrvM/4/fffC3Xr1hXkcrkQGBiot1vRs4YNGyZIpVLh1q1bBd7bmM9sSGZmpvDll18KISEhgpubmyCVSoWKFSsKffr0EXbu3Gn0M4mIqOQ5ffq0MHToUKFKlSqCXC4XnJ2dhUaNGgnTp0/X2W02OztbeP/99wV/f3/B0dFRaNu2rXD69Ol8d997fpffvL+/9+/f1ykfOnSo4OzsrFOWkJAg9O3bV/D09BTc3d2FN954Qzh+/LhRu+8dPnxYCAkJEZycnITy5csLI0eOFE6ePKl3bXZ2tjBy5EihfPnygkgk0ulz5Lej76NHjwRHR8cCdxEuTh+goN33AAhKpVK4c+eOUKFCBeHll1/W7kwnCJrd5bp37y6UK1dO+zny2ufEiRNC9+7dBRcXF8HV1VUYNGiQcPfuXb1nP7/7nrH9s/x231u4cKHeZzTUhzxz5ozQv39/oUKFCoJMJhN8fX2Fl19+Wfjmm2906h06dEho0aKFoFAoBF9fX2Hy5MnC8uXLjdp971njx48XAAhdunTRO/fgwQNhxIgRQoUKFQQnJyfhpZdeEg4cOKD3GfN22Nu2bZvePQztvnfr1i3h1VdfFTw8PARXV1ehU6dOwrlz54z+9ye/Hf3Wr18vNG3aVHBwcBBcXFyERo0a6fyeC4Ig7N+/X+jatavg6ekpyGQyoVKlSkLXrl0Nxk6UH5EgPNlaiYhKtBs3biAoKAgLFy7Md8FGInPKyclBYGAgXnrpJWzdutXW4RAREVEJ8fHHH2PmzJm4f/++dn0hIqKi4PQ9IiLScf/+fVy6dAlr1qzB3bt3dRbOJCIiIiIiMhcmpYiISMfOnTsxfPhw+Pn5ISoqSm8BdCIiIiIiInPg9D0iIiIiIiIiIrI6sa0DICIiIiIiIiKisodJKSIiIiIiIiIisjompYiIiIiIiIiIyOrK3ELnarUad+7cgaurK0Qika3DISIiIhsSBAGPHz9GxYoVIRbzu7riYj+LiIiIAOP7WGUuKXXnzh34+/vbOgwiIiIqQW7evInKlSvbOgy7x34WERERPauwPlaZS0q5uroC0DSMm5ubWe+tVCqxZ88ehIaGQiaTmfXe9BTb2fLYxtbBdrY8trF12HM7p6amwt/fX9s/oOKxVD/Lnn/H7Anb2TrYzpbHNrYOtrPl2XMbG9vHKnNJqbyh5G5ubhZJSjk5OcHNzc3ufmHsCdvZ8tjG1sF2tjy2sXWUhnbmVDPzsFQ/yyy/Y9nZwIQJmuNFiwCFwmzxlRal4d9le8B2tjy2sXWwnS2vNLRxYX0sLp5ARERERKVfbi4QFaV55ebaOhoiIiICk1JERERERERERGQDTEoREREREREREZHVMSlFRERERERERERWx6QUERERERERERFZHZNSRERERERERERkdUxKERERERERERGR1UltHQARERERkcU5OgJxcU+PiYiIyOaYlCIiIiKi0k8sBgIDbR0FERERPYPT94iIiIiIiIiIyOqYlCIiIiKi0i8nB5g8WfPKybF1NERERAQmpYiIiIioLFAqgU8/1byUSltHQ0RERGBSioiIiIiIiIiIbIBJKSIiIiIiIiIisjompYiIiIiIiIiIyOqktg6AiIiIiIiIiIjyp1ILOHotGQeu3sPZm4+QlauCg1QCbxcFRCLduoIgICktJ986giAgOV0JJ4UEzQK9MLRlIORS24xZYlKKiIiIqAT5+++/sXDhQpw4cQIJCQn46aef0KtXrwKv2b9/PyZMmIDz58+jYsWKeP/99zF69GidOj/88AOmTZuGa9euoVq1apg7dy569+6tUycqKgoLFy5EQkIC6tati8WLF6N169bm/ohEREQlhkot4PCVJHx/8iZuPciEQioucqKnoPNFqaOQiJD1SIz1t4/h7O1UKFWC2T9/dOw9zPvtAt5uHYSILnXMfv/C2DQpZalOFxEREZG9Sk9PR8OGDTF8+HC8+uqrhdaPi4tDly5d8NZbb+Hbb7/FoUOHEB4ejvLly2uvP3LkCAYMGIDZs2ejd+/e+Omnn9C/f38cPHgQzZs3BwBs2bIF48aNQ1RUFFq1aoVly5ahc+fOiI2NRZUqVSz6mYmIiAqSN0ro0LX7uP0gU+dccZJBdx5m4uTNh1CprfhhTCYGUh5Z9AmCACz7Ow4ArJ6YsmlSyhKdLiIiIiJ71rlzZ3Tu3Nno+t988w2qVKmCxYsXAwBq166N48eP49NPP9X2jxYvXoyOHTsiIiICABAREYH9+/dj8eLF+O677wAAixYtwogRIzBy5EjtNbt378bSpUsRGRlpxk9oI46OwLlzT4+JiMiiCkok5TEmoXT7YSZO33yEXLX5RwmRrhUH4jAxtJZVp/LZNClliU4XERERUVly5MgRhIaG6pSFhYVh1apVUCqVkMlkOHLkCMaPH69XJ69PlZOTgxMnTmDKlCk6dUJDQ3H48OF8n52dnY3s7Gzt+9TUVACAUqmEUqkszsfSkXevYt/zhRc0/1SpNC/SYbZ2pgKxnS2PbWweKrWAmLgUHL6ejDsPM/F8TkitUuHKTTHWxB+Bo1wCL2c58jJKCY+yLDbdjCxHLQBrD13D8JaBxb6Xsf/+2dWaUsZ0up5nrc5S3j2f/SdZBtvZ8tjG1sF2tryitrFaLUCpFqBWC1ALeS9N50wQBKgEaMrUApQqAQIEqNXQqas5r1uWnatCrlqAVCzS1hGe/FNzb2jrCs88MyktGx5Ock19aM5p6kLzbE3h03tq3mrvIUBAZo4KDzNz4e0sB4Cn98l7A009QYD2+rwyGCh7Wk+AWq3GjRtinNx5AWKx+Ol98+J4cpBXdjc1G3KpGG4O+t0QQ13Xiu4OGNO2qkk/Q2OVhn//EhMT4ePjo1Pm4+OD3NxcJCUlwc/PL986iYmJAICkpCSoVKoC6xgSGRmJmTNn6pXv2bMHTk5ORf1I+YqOjjb7PUkf29k62M6WxzbWUAvAlUciXHkEpGSLnvQRgLRcIEcFyCWAiwx4doDSg2zgv3QR1BDle18NMfD4sSXDJys7cOoifB7GFvs+GRkZRtWzq6SUMZ2u51m7swTwP37Wwna2PLaxddiqnQUBUD/5p+pJgiP3yT/z3j/7ylZrOit571UQaa99rAQUYs39dK578j45SwRXmSYp8fS8SHs+MRPwlGsSEnkxPXuP2+kieDto0hXPlgvP3Oe/NKCi0zPXas9LMOvkPqgF4EGOCC7Sp0mbvOc/2xaFd77IMDGQcNMid/Z3FhCQftEi9za2w1TSiZ6b6yA8ySA+W26ozvNlxtR5VkREBCZMmKB9n5qaCn9/f4SGhsLNzc20D1EApVKJ6OhodOzY0eCXkEbJyYH4k08AAOopUwC53GzxlRZmaWcqFNvZ8kprG+eNXDp4LQnnbj9CZo4aDjKxzgglIG9ntRxkK9VIz8lFXHImRyyR0Vo3qoUuZhgplTcgqDB2lZQCjOt0PctanSWg9P7Hr6RhO1se27hwgiAgS6lGrloNpUozyiVXLSA9OxdqQXOcq9L880FGDmQSMZQqtaaeSsDdx9mQi4HzsbF4oWYtCCIxUjOVeJiphJuD7Mn91Nr7nE9IRaCnE3LVT5+Vq1bj9M1HqFHBBWpB0InjYuJj+LgqIJWItNeo1AIeZGhGhUjEIqjsbF5+YmbhyaJb6YXfJy23+EknkQiQiEQQiYDcJyOc3B2lED8pk4hE2mOxSASxWATxk+ObDzLh5SyHp5MMIpEIYjF064o0dUWip9coVWrcTMlELV9XQATNeTytl3etCJp/6tfR1MvMUeFRlhL+Hnnr6WiuFeFpX1b0TFleYd55zT91ywBAUKtx47//EBQYCIlErL1HXj2InrsvgAcZSsilYrgqnumKPPejyXtbwVWBLsGVi/1zM8TYDlNJ5uvrqzea6d69e5BKpfDy8iqwTt6Xfd7e3pBIJAXWMUShUEChUOiVy2Qyi/z9KNZ9c3KAOXMAAJIpUwD+fcuXpX5+pIvtbHkluY3zW2+poAW5T996xOQSWZRYBAxrVQ0yM6wpZey/e3aVlDKm0/U8a3eWLH1veortbHm2bmNBEJCdq0a2Ug2lWg2lSo2MHBWylCqo1AKUKjWyc9VISsuBXCJ+kiBS47/kDLg7ypCrEqBUq5GcloPHWUq4OsiQq1JDqRZw9tZDBHg5I1elflJPQOydVHi7yCERi7TXpqTn4GGGEh5OT++Xl2wyDwkQd8WomqdvGt51I+bGA4Pldx9nGywHUGhCSiEVQyIWaV9SsSZZcu9xNoK8nTXloifnJJpzFxJS0biKh/a9VKxJxuT989q9NNSv5A7Jk/d51+e9bqZkoIaP65NyPL32Sb17j7NRxdNJk/ARa+pIxOKn9UUiZOSo4Oms+RmKRICgViHm6FG0atUSCpkMYpEIAgQ4yiRPEkWiJ/fTJOqeTQpJJWLt5xSJnj2f/xchZZVSqcSuXXHo0rmW3f132d7iNSQkJAQ7duzQKduzZw+Cg4O1ny8kJATR0dE660rt2bMHLVu2BADI5XI0adIE0dHR6N27t7ZOdHQ0evbsaYVPQURk/3Jy1Vh3OA4xcSnIyM6F13OLduclnBJSs3AzJZMLd1OJ81brIKsucg7YWVLKmE4XEZmfIAhIzcpFdq4KOblqPM7KRU6uJgGUlp2LR5lK7WiOuKR0uDvKkJ2rSQY9yMiBq4MUSpUap+IfIsDLCbkqzZDimykZ8CvnAGWuJsF0PSkdbg7SJwknATkW3pv13G39ERJJaYYTOXkjjPIjEgEysVgbs4+bAlKxWJugiUtKR0P/cpA+SbRIJSJcvZcGb0kmqlT0g1wmgUQs0ibhArycIM1LCEnEEIuA5LQcBHo7a6/XnNeMsKpYzgESsfjpNWIRlGoB5RxlOoklTbJGBCe5RKdMKhZDLAakYk0iprRQKpVIigUa+Zfj3wmyG2lpabh69ar2fVxcHE6fPg1PT09UqVIFERERuH37NtavXw8AGD16NL7++mtMmDABb731Fo4cOYJVq1Zpd9UDgPfeew9t2rTB/Pnz0bNnT/zyyy/Yu3cvDh48qK0zYcIEDB48GMHBwQgJCcHy5csRHx+P0aNHW+/DExGVMHkjmg5cvYezNx/lu0vcuTuPcO1+6ZgSTmWPSAS83ToIEV3qWP3ZNk1KWaLTRVQWZSlVyM7VJIjSszUJo7upWRCJgMRH2dqFli8mPoaXsxzZuWrEJ2cgU6mCk1yC7Fw1jl1PRlB5Z+TkqnHrQSYycqSYevIPKHPNmxy6mKi7EGJyeo7O+9Ss3HyvlUvEkElESM9RobyrAk5yiXZaXEpaDupWcoNMIoZMIsa1+2maUTtPkjrZShUEAJU9HLXJouS0HFQt7wyZRKQty1KqUMHVATKJ5r1MIoZIBDjJJVBIJZCKRdpzCqkYcqkYMrEY4iIkcjSjS3ahS5eGTJgQkdbx48fRvn177fu8ZQiGDh2KtWvXIiEhAfHx8drzQUFB2LVrF8aPH48lS5agYsWK+PLLL3V2Jm7ZsiU2b96Mjz76CNOmTUO1atWwZcsWNG/eXFtnwIABSE5OxqxZs5CQkIB69eph165dCAgIsMKnJiKyHkNT5wxNm0t4xClzVDKJAQR4OcLP3VEvQQrkPw302fPJ6Uo4KSRoFuiFoS0DrT5CKo9Nk1KW6HQR2YvHWUqkZ6vwKFOJx1lKZCnVuJGcDplEhMRH2XiUqYRMKsLp+Ieo4OaAzBwVjsUlI8jbGRcTHgMiwFEmwaNM8+0c9fzIofRs/e2y5VLNaJyMHBUCvZwgl4rxMEMJJ7kEVbycIZdoRgU1CfCATCJGTq4aIhHg7+EEmVSMlPQcVC/vAplU819FqVgMT2e5Jrkj0UzFcnOUapNLcokYTgpNMohTpoioLGjXrp12zUxD1q5dq1fWtm1bnDx5ssD79u3bF3379i2wTnh4OMLDw42Kk4ioJFKpBRy5loxf/xPhj21nIBKJdf4HPT1bhetJ6Uw0kUEiAC9UcEJNX/ciJXoKOl+UOgqJCFmPUlCpkh+kEgkqeTiiZTVvtKjqVWpmN9g0KWWpTheRpeUtcn3vcRaS0nKQmaNCXFIaVGoB/6VkICU9R7N2jSDg0LVk+Hs44m5qNm4/zISrQorH2fmPBirM2VtP1xXKyTU8gsnXzQG5ajVSM3PRoLI77jzMRE1fV7g7ynDnYRZerFIOCqkY2blquDlIUcHVAQqZ5n2lco4QQ41jR4+g6ytt4eyggFwqhrNcCgeZmIkhIiIiIrI6YxYG1004SYA7d20XMFlUVW9H1K9UDkDxk0EikajEJnvKwswKu1pTishSBEHAwwwlktKyEZeUjjsPM5GVq8aZmw9RzkmGvy8nQSYR4c6jrHwTQQW5/8yC088npFwUUqRl56J6BRc4ySW4/SATzat6IkuphrNCigBPJySn56BuRTfNkEoBqOThCJFIsyOVQiqBi0IKZ4UUMol5RhMplUrcOw8EejmX2v/4EREREVHJkF/CCdD00y/dTcPV+2mw8HKjZGFSEfBilXKoVM5RW2ZsQqmkTDUj82NSiko1lVpAcno24pMzcO9xNi7ffYz/kjOgUgs4dDUJ5V0VemscmUosAtQCUL+SO24/zETDyu5QCYCXsxyBXs7wcpFDqVKjWnkXyKVieDnL4eogg4uDFC4K/itIRERkFQ4OQEzM02MispqCRjkx4WQ/nh2dBAC5KhUu/5cIF/dycJRLDSaUSvIoJCoZ+H/EZNdyVWrcSM7Av7cfIu5+Om4+yMTjLCX+vpwED2cZ7qYa3kktz/OLbAOakUsSsQieznLUq+SOLKUKTQM9oBaAQC8nVHBzgIeTHOVdFXB8smMaERERlXASCdC0qa2jICqVmHSyH3mjlSq6OxR7IeynU8tacHYFFRmTUlTi5ao0u8EdvJqEI9eTcf72I9xIluK9I3sKvO7ZhJSTXIKMHBUaVnaHXCqGn7sj6lVyg6NMglp+bijvooC3q4Ijl4iIiIiInlPQbnUJqVm4mZKJXDUXDremgnZfe35KXHlXB1T25GglKpn4f+BUovyXnI7o2Ls4ci0ZJ+If4GGGaTvLNazsjhd8XFGvkjvKuypQ09cVlT0coZBKLBQxERER2YWcHOCLLzTH770HyOW2jYeohHk+8cSkk/U8u9aSvS7ITVRUTEqRTajUAk7ffIjd5xOx698E3HpuQcOCVHR3gJ8sA71b1UPjAC/U9HXlf5CJiIioYEol8P77muPwcCalqEzKSzwduHoPZ28+0iY+clRqnLn16MmudWQOYhHQ2N8dlTyctGXPJpwcZVI0rFwOrWowwURlG5NSZBVxSenYevwmfvs3ATeSM4y65kX/cgip5oWQql5oGugJR7lmtJN27nJwZc5dJiIiIiJ6Rn7rO915mInTTDwVS0FT5gCOZCIqCialyOxUagGHryVh6/Fb2HHmTqH13R1laFHVE53q+aJ9zQoo58RvLomIiIiICmIo+cTEk+kKW/ibiSYiy2JSiszi3O1HWPLnVfx2LrHQuqF1fNCncWW0q1keDjKu9URERERElB9Daz1xRzvj5Jdw4sLfRCUHk1JUJFlKFX45fRuRv10scDHyquWd0b1BRfRv6o9K5RytGCERERERkX3JyVVj3eE4xMSlICM7F9lc68kgsQhonM/C4OVdHeDnroDk/lW8O7ATHBSchUFUkjEpRUbLzlVh1cE4fLr7EvLbfMPDSYbwdtUxoJk/3By43hMRERER0fPUAnDkWjKO3nignXp37s4jXLtv3NqrpdmzCSfg6eLg2So1/D2c8GrjymhZ3bvA0U2aNWivcAQUkR1gUooKJAgCdv2biHe/O5lvIqpLfV+M7VADtXzdrBscEREREVEJ9+z0u1spGbiYmIqr9yRQHz1h69BswtAoJ1MSTkRUujApRQbde5yF8VtO49DVZIPn32lfDaPbVoMrR0MRERGRPXBwAP788+kxkZmZtvB46U265K3jxKQTERmDSSnSsf/yfQxdHWPw3KuNKyOiSy14uyisHBURERFRMUkkQLt2to6CSpFnk1D/xKWUmV3vDC0e7iiTomHlcmhVgwuHE5FpmJQiCIKArcdv4oMf/tU7V6mcI74c9CKaBHjaIDIiIiIiItt7fhRU/iOgSgepCGjo7w6FVMKkExFZFJNSZZggCPj1bALe/e6U3rmhIQGY0rk2HOUSG0RGREREZGZKJbB8ueb47bcBGZcgoPyVhVFQYgABXo7wc3eEt4sCYrEIlTwc0bIaE09EZD1MSpVRp28+RK8lh/TK5/Wuj0HN/CES8Y8QERERlSI5OcD//qc5HjaMSSnSkZOrxrrDcYiJS8HNlAxcS0ovNUmoZ9d4EomYeCKikoVJqTLm3uMsdFp8ACnpOTrlH3SqhbfbVOUfJiIiIiIq9Z4dCbX7fCKu3c+wdUjF8uyOdgCYfCIiu8GkVBmhUguY/Wss1h6+oVM+rGUgpnapDblUbJvAiIiIiIgsrLRMx3t2rSfuaEdEpQGTUmXA8Rsp6PvNEZ2yZkGeWD2sKVwU/BUgIiIiotLl2SRUTFwKTt98hFy1fSWhxCKgsb87Kns6c9QTEZVazEiUYhk5uRi6Ogb/3HigU75nfBu84ONqo6iIiIiIiMzLnkdCPTv1jtPuiKisYVKqlDp2PRkDlh/VKRvboQbGdagBMf+4EREREVEpoFIL+CL6Mpb+fc0uklASANXKOyEnIw0NqlVEv+AqnHpHRGUak1Kl0KwdsVh9KE77XiIW4WhEB5R3VdgwKiIiIiKi4nl+RNSJ+IcoqbPy8na9q+zhpDP6Sa3Kxa5du9ClSwPIuAskEZVxTEqVIklp2Qies1enbErnWhjVpipEIn77QkRERGWYQgH8+uvTY7IL9jItL78ElKERUGqVDQIkIiqhmJQqJU789wCvLj2sU/bnpHYI8na2UUREREREJYhUCnTtausoyEgqtYCv/riCpfuvITtXbetw9EhFQKMADzQL8uT6T0RExcCkVCnw/YlbmLTtjPZ9u5rlsXpoU64dRURERER2I29U1PqjN7D3wl2oSkguypRRUEREZBompeyYIAiY9Wss1hy6oS2b2qUW3m5TzXZBEREREZVESiWwcaPm+PXXAa7lUyI8m4jad/FeiZiaJxYBTaqUQ7OqXkxAERFZGJNSdipXpcbob09i74W72rJvRzTHSzW8bRgVERERUQmVkwMMH6457tePSSkbK0nT8zgVj4jIdpiUskNqtYBha/7BwatJ2rK9E9qgegVXG0ZFRERERFSwvGTUkj+vQmnDbfOqlXdCp3p+TEIREdkYk1J2RhAEvL3huE5Cat/Etqha3sWGURERERERGWbrtaIkAKr7uKCKlxOaBXphaMtAyKVi6wZBREQGMSllZz78+Rz2Xrinfb9/cjsEeHGHPSIiIiIqWfJGRX2z/xqyrDxFjyOhiIjsA5NSdmTlgevYdCxe+/7A++3h7+lkw4iIiIiIiHTlJaO+/vMqcq04RU8qBnq+WBGRfRpyJBQRkZ1gUspO7L98H3N2Xnj6fnI7JqSIiIiIqMSw9npRXKCciMj+MSllB67dT8PQ1THa99+PDuGUPSIiIiIqEayZjJKJgQ61fTA4JJBJKCKiUoBJqRLucZYSHT7br33/cfc6CA70tGFERERERHZIoQC2bn16TMVizcXLmYgiIiq9mJQq4d7//qz2uM0L5TG0ZaDtgiEiIiKyV1Ip0K+fraMoFXadTcD7P5xFWnauxZ4hEQMdmYgiIir1mJQqwX44cQu/nUvUvv/mjcYQifgHmYiIiIhsY+7OWKw4EGex+yskIoxpVw3vdniBiSgiojKASakS6lLiY0zcdkb7Pnp8GzjJ+eMiIiIiKpLcXOCnnzTHvXtrRk6R0VRqAWM3ncTOZ74wNReOiiIiKrv417gEysjJRdjiv7XvPx/QEDV8XG0YEREREZGdy84G+vfXHKelMSllpLxFzL/edwW5Zl7DXCEVYUxbjooiIirL+Ne4BFq894r2uEt9X/RuVNmG0RARERFRWWPJHfVe9HfD5LDaHBVFRERMSpU0N5LSsfzv69r3kX0a2DAaIiIiIiprfjuXiPd/OIesXPNuqecsF2Nh34bo0qCiWe9LRET2i0mpEkSlFhD6zLS9TW81h7ujzIYREREREVFZ8vMNEf48crbwikbielFERFQQJqVKkF9O30bOk2+kejSsiJbVvG0cERERERGVBSq1gHc3n8afCWKz3E8mEeEd7qJHRESFYFKqhFCrBUzY+nS3vS8Gvmi7YIiIiIiozNh1NgHjt5xCtkoAULwEEpNRRERkCialSoj5v1/UHn/aryFEIv4RJyIiIiLLUakFvPfdKfz6b0Kx7yUVA/9rX53JKCIiMgmTUiWASi1g2TOLm/d6kYs/EhEREZmVXA6sWfP0uIzTHR1VPF3r++DLQU2YjCIiIpMxKVUCTNx6Wnv88zutIJWYZy4/ERERET0hkwHDhtk6ihJh7s5YrDgQV+z7OMjEWNSPu+kREVHRMSllYynpOfj59B0AgEgE1KvoZuOIiIiIiKi0mv3reaw6eKNY9+C6UUREZC4ckmNj83Zd0B5vGtmCo6SIiIgIUVFRCAoKgoODA5o0aYIDBw4UWH/JkiWoXbs2HB0dUbNmTaxfv17nfLt27SASifReXbt21db5+OOP9c77+vpa5PPZRG4usHOn5pWba+tobMIcCamu9X1wcXZnjOtYkwkpIiIqNo6UsqGbKRn4/sQtAICbgxTBgR42joiIiIhsbcuWLRg3bhyioqLQqlUrLFu2DJ07d0ZsbCyqVKmiV3/p0qWIiIjAihUr0LRpU8TExOCtt96Ch4cHunfvDgD48ccfkZOTo70mOTkZDRs2RL9+/XTuVbduXezdu1f7XiKRWOhT2kB2NtCtm+Y4LQ2Qlq1ucHETUgqpCJ/3f5FT9YiIyKzK1l/jEmbNoRva4wV9G0LGUVJERERl3qJFizBixAiMHDkSALB48WLs3r0bS5cuRWRkpF79DRs2YNSoURgwYAAAoGrVqjh69Cjmz5+vTUp5enrqXLN582Y4OTnpJaWkUmnpGh1FUKkFjN10EjvPJRb5HlzInIiILIVZEBv5Lzkdqw9pFpis7OGIDrUr2DgiIiIisrWcnBycOHECoaGhOuWhoaE4fPiwwWuys7Ph4OCgU+bo6IiYmBgolUqD16xatQoDBw6Es7OzTvmVK1dQsWJFBAUFYeDAgbh+/brB68k+7DqbgDrTfityQspBJkbUa42w5PVgJqSIiMgiOFLKRn49m6A9HvtyDY6SIiIiIiQlJUGlUsHHx0en3MfHB4mJhhMLYWFhWLlyJXr16oXGjRvjxIkTWL16NZRKJZKSkuDn56dTPyYmBufOncOqVat0yps3b47169fjhRdewN27dzFnzhy0bNkS58+fh5eXl8FnZ2dnIzs7W/s+NTUVAKBUKvNNiBVF3r2KdU+lErJn72fG+EqiT36/hFWH/ivy9Z3rVsDn/RtCIhaZ9WdJZvp9pgKxja2D7Wx59tzGxsbMpJQN3EvNwsLdlwAAgV5OeLVJZRtHRERERCWJSKQ7KkUQBL2yPNOmTUNiYiJatGgBQRDg4+ODYcOGYcGCBQbXhFq1ahXq1auHZs2a6ZR37txZe1y/fn2EhISgWrVqWLduHSZMmGDw2ZGRkZg5c6Ze+Z49e+Dk5FTo5zRVdHR0ka+VZGXhyYpS2L17N1TPjS4rTX6KE+GvRDEA00c3SSBgcA01Grndwe7f75g/ONIqzu8zGYdtbB1sZ8uzxzbOyMgwqh6TUjaw//J97XG/YH8OhyYiIiIAgLe3NyQSid6oqHv37umNnsrj6OiI1atXY9myZbh79y78/PywfPlyuLq6wtvbW6duRkYGNm/ejFmzZhUai7OzM+rXr48rV67kWyciIkInYZWamgp/f3+EhobCzc2t0GcYS6lUIjo6Gh07doRMJiv8AkPS07WHYWFhwHNTF0uLyN8u4q/E+CJdK5cAkcEqdA4rRjtToczy+0wFYhtbB9vZ8uy5jfNGTxeGSSkry8xRYfL3ZwFodtwb3baajSMiIiKikkIul6NJkyaIjo5G7969teXR0dHo2bNngdfKZDJUrqwZfb1582Z069YNYrHu8gBbt25FdnY23njjjUJjyc7OxoULF9C6det86ygUCigUCoOxWKLzXKz7PnOdTCbTeV9a7Dp7B6sPFy0hBQCL+jWE6r8TFvv5kS62s+Wxja2D7Wx59tjGxsbLpJSVnfjvgfa4x4sVOUqKiIiIdEyYMAGDBw9GcHAwQkJCsHz5csTHx2P06NEANKOTbt++jfXr1wMALl++jJiYGDRv3hwPHjzAokWLcO7cOaxbt07v3qtWrUKvXr0MrhE1adIkdO/eHVWqVMG9e/cwZ84cpKamYujQoZb9wNYilwNff/30uJRRqQVM2HqmSNc6ycVY1P9FdKjpjV1FX4aKiIjIZExKWZFaLeCDH85q30/rVseG0RAREVFJNGDAACQnJ2PWrFlISEhAvXr1sGvXLgQEBAAAEhISEB//dDSMSqXCZ599hkuXLkEmk6F9+/Y4fPgwAgMDde57+fJlHDx4EHv27DH43Fu3bmHQoEFISkpC+fLl0aJFCxw9elT7XLsnkwHvvGPrKCxCpRbw+vIjyMpVm3xtl3o++Oq1JlzQnIiIbIJJKSu6npSO2w8zAQAdalWAQqq/+CgRERFReHg4wsPDDZ5bu3atzvvatWvj1KlThd7zhRdegCAI+Z7fvHmzSTFSybDrbALGbzmFbFX+P9v8vNU6EB92rWuBqIiIiIzDpJQV7TyboD1ePPBF2wVCREREVNaoVMCBA5rj1q0BAzsT2pu5O2Ox4kCcydc5yMRY1K8hujSoaIGoiIiIjMeklJVk5qjw+d7LAIDKHo5wdbCvRcqIiIiI7FpWFtC+veY4Lc3ud9+b/et5rDp4w+TrWgR6YOPbIVzXlIiISgRx4VXIHO4/ztYef9avoQ0jISIiIiJ7VtSElEIiYkKKiIhKFCalrGTQiqPa4yYBHjaMhIiIiIjsVVETUgDw+YBGTEgREVGJwqSUlaSk5wAAejSsCKmEzU5EREREppm7s+gJqbdaB6FLAz/zBkRERFRMzI5YwdbjN5GpVAEAJofVtHE0RERERGRvdp29gxUHbhTp2hEvBeLDrnXMGxAREZEZcKFzK1i2/5r22MNZbsNIiIiIiMjeqNQCPvrlXJGufat1ID7sWtfMEREREZkHk1IWJggCbj7IBAB8PqAhXBRsciIiIiIyXkxcClLSlSZd4yATY1G/hujSoKKFoiIiIio+ZkgsbN6uC8jJVQMA/D2cbBwNERERURklkwELFjw9tiPL/r5qUv0u9Xzw1WtNuKg5ERGVeExKWdi/tx9pj2v7udkwEiIiIqIyTC4HJk+2dRQmm/3refx1Kcno+iNeCsS0bpyuR0RE9oELnVvQw4wcHL2eAgD4+rVGcObUPSIiIiIy0uxfTdttr90L3kxIERGRXWGWxIIW772iPVZIJTaMhIiIiKiMU6mAkyc1x40bA5KS3Tebu9O0hBQAjGpb3TLBEBERWQiTUhb0MCMHACASAS9V97ZxNERERERlWFYW0KyZ5jgtDXB2tm08Bdh19g5WHLhh0jVeznI0C/K0TEBEREQWwul7FpLwKBM/n74DAPiwS204ykv2t3FEREREZHsqtYDJP5w1+brZPetxYXMiIrI7TEpZyHcxN7XH5ZzkNoyEiIiIiOzF2O9OIj1bZdI1b7UOQpcGfhaKiIiIyHKYlLKQ7FxNZ8LdUYZu7CQQERERUSHm7jyPnf8mmnTNiJcC8WHXOhaKiIiIyLKYlLKAOw8zsWz/dQBA/+DKcJBx6h4RERER5a8o60iNeCmQu+0REZFdY1LKAvZduq899nN3tGEkRERERFTSqdQCPvrlnEnXMCFFRESlAZNSFqBSCwAAHzcFhrYMtG0wRERERFSixcSlICVdaXT9rvV9mZAiIqJSQWrrAEobQQDO3U4FADQL8uIuKEREREQlgUwGzJjx9LgEWf73VaPrOssl+HJQYwtGQ0REZD1MSpnZf2nAz+cSAAAS5qOIiIiISga5HPj4Y1tHoWfX2Tv481KS0fUX9m3ILz2JiKjU4PQ9M3uY87ST8FrzABtGQkREREQlmUotYPIPZ42u375meXThrs5ERFSKcKSUhTQL9ESzIE9bh0FEREREAKBWAxcuaI5r1wbEtv9u9ut9V5CerTK6/tttqlkwGiIiIutjUoqIiIiISr/MTKBePc1xWhrg7GzTcFRqAcv+vm50fS9nOb/wJCKiUsf2XxEREREREZUx4zafREaO8aOkZvesx7WkiIio1GFSysxOJLGzQERERET523X2DnacTTS6/lutg7iWFBERlUo2T0pFRUUhKCgIDg4OaNKkCQ4cOFBg/Y0bN6Jhw4ZwcnKCn58fhg8fjuTkZCtFW7jrjzVJKYXM5k1LRERERCWMqYubd63viw+71rFgRERERLZj08zJli1bMG7cOHz44Yc4deoUWrdujc6dOyM+Pt5g/YMHD2LIkCEYMWIEzp8/j23btuGff/7ByJEjrRx5/tr4qlHHzwUTOr5g61CIiIiIqIQxZXFzR5kYXw5qbOGIiIiIbMemSalFixZhxIgRGDlyJGrXro3FixfD398fS5cuNVj/6NGjCAwMxNixYxEUFISXXnoJo0aNwvHjx60cef7CKgsIrV0Bjap42DoUIiIiIipBTF3cfHTb6lxHioiISjWbJaVycnJw4sQJhIaG6pSHhobi8OHDBq9p2bIlbt26hV27dkEQBNy9exfff/89unbtao2QiYiIiIiKzJTFzV0UUvzv5eoWjoiIiMi2pLZ6cFJSElQqFXx8fHTKfXx8kJhoeOHHli1bYuPGjRgwYACysrKQm5uLHj164Kuvvsr3OdnZ2cjOzta+T01NBQAolUoolUozfJKn8u4nElRmvzc9lde2bGPLYRtbB9vZ8tjG1mHP7WyPMVMRyWTApElPj63M1MXNF7zagKOkiIio1LNZUiqPSKT7x1YQBL2yPLGxsRg7diymT5+OsLAwJCQkYPLkyRg9ejRWrVpl8JrIyEjMnDlTr3zPnj1wcnIq/gcwIDDrGnbtumaRe9NT0dHRtg6h1GMbWwfb2fLYxtZhj+2ckZFh6xDIWuRyYOFCmzza1MXNuzXw4257RERUJtgsKeXt7Q2JRKI3KurevXt6o6fyREZGolWrVpg8eTIAoEGDBnB2dkbr1q0xZ84c+Pnp//GOiIjAhAkTtO9TU1Ph7++P0NBQuLm5mfETab5tjY6Oxg2Hagh/uaZZ701P5bVzx44dIbPBN51lAdvYOtjOlsc2tg57bue8EdRElmTq4uZfDGxk4YiIiIhKBpslpeRyOZo0aYLo6Gj07t1bWx4dHY2ePXsavCYjIwNSqW7IEokEgGaElSEKhQIKhUKvXCaTWazjLIgkdtcpt0eW/BmSBtvYOtjOlsc2tg57bGd7i5eKQa0G8nZ4rlIFEFtnaVWVWsCaQzeMrs/FzYmIqCyx6fS9CRMmYPDgwQgODkZISAiWL1+O+Ph4jB49GoBmlNPt27exfv16AED37t3x1ltvYenSpdrpe+PGjUOzZs1QsWJFW34UIiIiIirJMjOBoCDNcVoa4OxslcfGxKXgYaZxa5dxcXMiIiprbJqUGjBgAJKTkzFr1iwkJCSgXr162LVrFwICAgAACQkJiM/7RgvAsGHD8PjxY3z99deYOHEiypUrh5dffhnz58+31UcgIiIiIspXYmqW0XW5uDkREZU1Nl/oPDw8HOHh4QbPrV27Vq/s3XffxbvvvmvhqIiIiIiIiu/QlftG1QsJ8uTi5kREVOZYZzI9EREREVEZo1IL2PlvglF1+zf1t3A0REREJQ+TUkREREREFvD1vivIVKqNquvr7mjhaIiIiEoeJqWIiIiIiMxMpRaw7O/rRtUt5yRDsyBPC0dERERU8jApRURERERkZl/vu4KMHJVRdYe3DOIC50REVCbZfKFzIiIiIiKLk0qBvM11pJbtApsySspJLsH/Xq5u0XiIiIhKKialiIiIiKj0UyiAJUus8ihTRkmNalONo6SIiKjM4vQ9IiIiIiIz4SgpIiIi43GkFBERERGVfoIAJCVpjr29AZFlRidxlBQREZHxmJQiIiIiotIvIwOoUEFznJYGODub/REqtYA1h24YVZejpIiIiDh9j4iIiIjILGLiUvAwU2lUXY6SIiIiYlKKiIiIiMgs9sYmGlWPo6SIiIg0mJQiIiIiIiomlVrA5uM3jarLUVJEREQaTEoRERERlTBRUVEICgqCg4MDmjRpggMHDhRYf8mSJahduzYcHR1Rs2ZNrF+/Xuf82rVrIRKJ9F5ZWVnFei499fW+K0jPLnyBcxeFlKOkiIiInmBSioiIiKgE2bJlC8aNG4cPP/wQp06dQuvWrdG5c2fEx8cbrL906VJERETg448/xvnz5zFz5ky888472LFjh049Nzc3JCQk6LwcHByK/Fx6ypQFzvsHV+YoKSIioieYlCIiIiIqQRYtWoQRI0Zg5MiRqF27NhYvXgx/f38sXbrUYP0NGzZg1KhRGDBgAKpWrYqBAwdixIgRmD9/vk49kUgEX19fnVdxnktPmbLAecc6voVXIiIiKiOYlCIiIiIqIXJycnDixAmEhobqlIeGhuLw4cMGr8nOztYZ8QQAjo6OiImJgVL5NFGSlpaGgIAAVK5cGd26dcOpU6eK9Vy7I5UCQ4dqXlKpWW9t7ALn5ZxkaBbkadZnExER2TPz/kUmIiIioiJLSkqCSqWCj4+PTrmPjw8SEw0nPsLCwrBy5Ur06tULjRs3xokTJ7B69WoolUokJSXBz88PtWrVwtq1a1G/fn2kpqbiiy++QKtWrXDmzBnUqFGjSM8FNAmx7Oxs7fvU1FQAgFKp1EmIFVfevYp1T7EYWLHi2ZsWMyoNlVrA5n+MW+B8aIsqUKtyoS586SmbMEs7U6HYzpbHNrYOtrPl2XMbGxszk1JEREREJYxIpLvmkCAIemV5pk2bhsTERLRo0QKCIMDHxwfDhg3DggULIJFIAAAtWrRAixYttNe0atUKjRs3xldffYUvv/yySM8FgMjISMycOVOvfM+ePXBycir8g5ooOjra7Pcsrt9vipCeIym0noNYQGDGJezadckKURVPSWzn0ojtbHlsY+tgO1uePbZxRkaGUfWYlCIiIiIqIby9vSGRSPRGJ927d09vFFMeR0dHrF69GsuWLcPdu3fh5+eH5cuXw9XVFd7e3gavEYvFaNq0Ka5cuVLk5wJAREQEJkyYoH2fmpoKf39/hIaGws3NzajPbAylUono6Gh07NgRMpmsaDcRBCCvg+zkBBSQbDOWSi1g+id/AsgttO7A5gHo1qVWsZ9pSWZpZyoU29ny2MbWwXa2PHtu47zR04VhUoqIiIiohJDL5WjSpAmio6PRu3dvbXl0dDR69uxZ4LUymQyVK1cGAGzevBndunWDWGx4+VBBEHD69GnUr1+/WM9VKBRQKBQGY7FE57lY901PBzw8NMdpaYCzc7HjOX4tGY8yC09IAUBYvYp28z8Ulvr5kS62s+Wxja2D7Wx59tjGxsbLpBQRERGRGWzYsAHffPMN4uLicOTIEQQEBGDx4sUICgoqNKH0rAkTJmDw4MEIDg5GSEgIli9fjvj4eIwePRqAZnTS7du3sX79egDA5cuXERMTg+bNm+PBgwdYtGgRzp07h3Xr1mnvOXPmTLRo0QI1atRAamoqvvzyS5w+fRpLliwx+rmk797jLKPqcYFzIiIiw5iUIiIiIiqmpUuXYvr06Rg3bhzmzp0LlUqzknW5cuWwePFik5JSAwYMQHJyMmbNmoWEhATUq1cPu3btQkBAAAAgISEB8fHx2voqlQqfffYZLl26BJlMhvbt2+Pw4cMIDAzU1nn48CHefvttJCYmwt3dHY0aNcLff/+NZs2aGf1c0uftoj9KzJBhIYGQiIs/XZCIiKi0YVKKiIiIqJi++uorrFixAr169cInn3yiLQ8ODsakSZNMvl94eDjCw8MNnlu7dq3O+9q1a+PUqVMF3u/zzz/H559/Xqznkr6YuGSj6jUN5CgpIiIiQwwvNEBERERERouLi0OjRo30yhUKBdLT020QEVmaSi1g3eH/jKqblJ5t4WiIiIjsE5NSRERERMUUFBSE06dP65X/9ttvqFOnjvUDIouLiUvBw0ylUXUruDpYOBoiIiL7xOl7RERERMU0efJkvPPOO8jKyoIgCIiJicF3332HyMhIrFy50tbhkQXsjU00qh4XOSciIsofk1JERERExTR8+HDk5ubi/fffR0ZGBl577TVUqlQJX3zxBQYOHGjr8AgAJBKgb9+nx8WgUgv46fRto+oObxnERc6JiIjywaQUERERkRm89dZbeOutt5CUlAS1Wo0KFSrYOiR6loMDsG2bWW4VE5eClPTCp+65KKT438vVzfJMIiKi0ohJKSIiIqJiiouLQ25uLmrUqAFvb29t+ZUrVyCTyRAYGGi74MjsjJ261z+4MkdJERERFYALnRMREREV07Bhw3D48GG98mPHjmHYsGHWD4gsxpSpex3r+Fo4GiIiIvvGpBQRERFRMZ06dQqtWrXSK2/RooXBXfnIBtLTAZFI80pPL/JtjJ265+Us5wLnREREhWBSioiIiKiYRCIRHj9+rFf+6NEjqFQqG0RElmLs1L2eL1bk1D0iIqJCMClFREREVEytW7dGZGSkTgJKpVIhMjISL730kg0jI3Pi1D0iIiLz4kLnRERERMW0YMECtGnTBjVr1kTr1q0BAAcOHEBqair27dtn4+jIXDh1j4iIyLw4UoqIiIiomOrUqYOzZ8+if//+uHfvHh4/fowhQ4bg4sWLqFevnq3DIzPh1D0iIiLz4kgpIiIiIjOoWLEi5s2bZ+swyEI4dY+IiMj8mJQiIiIiMoOHDx8iJiYG9+7dg1qt1jk3ZMgQG0VF5sKpe0RERObHpBQRERFRMe3YsQOvv/460tPT4erqCpHo6dQtkUjEpFRJIJEAXbo8PTYRp+4RERGZH5NSRERERMU0ceJEvPnmm5g3bx6cnJxsHQ4Z4uAA7NxZpEs5dY+IiMgyuNA5ERERUTHdvn0bY8eOZUKqlOLUPSIiIstgUoqIiIiomMLCwnD8+HFbh0EWwql7RERElsHpe0RERETF1LVrV0yePBmxsbGoX78+ZDKZzvkePXrYKDLSSk8HKlTQHN+7Bzg7G3UZp+4RERFZDpNSRERERMX01ltvAQBmzZqld04kEkGlUlk7JDIkI8PkSzh1j4iIyHKKlJRSqVT4+eefceHCBYhEItSuXRs9e/aEpAg7mRARERHZO7VabesQyEI4dY+IiMhyTE5KXb16FV27dsWtW7dQs2ZNCIKAy5cvw9/fHzt37kS1atUsEScRERERkVVx6h4REZFlmZyUGjt2LKpWrYojR47A01MzRDk5ORlvvPEGxo4di51F3GqXiIiIyJ6lp6dj//79iI+PR05Ojs65sWPH2igqKg5O3SMiIrIsk5NS+/fvx9GjR7UJKQDw8vLCJ598glatWpk1OCIiIiJ7cOrUKXTp0gUZGRlIT0+Hp6cnkpKS4OTkhAoVKjApZafuPc4yqh6n7hERERWN2NQLFAoFHj9+rFeelpYGuVxulqCIiIiI7Mn48ePRvXt3pKSkwNHREUePHsV///2HJk2a4NNPP7V1eFRE3i4Ko+p1qO1j4UiIiIhKJ5OTUt26dcPbb7+NY8eOQRAECIKAo0ePYvTo0dzumIiIiMqk06dPY+LEiZBIJJBIJMjOzoa/vz8WLFiAqVOn2jo8AgCxGGjbVvMSG9kFFoy8t7H1iIiISIfJSakvv/wS1apVQ0hICBwcHODg4IBWrVqhevXq+OKLLywRIxEREVGJJpPJIBJppm/5+PggPj4eAODu7q49JhtzdAT++kvzcnQ06pKk9Gyz1iMiIiJdJq8pVa5cOfzyyy+4cuUKLl68CEEQUKdOHVSvXt0S8RERERGVeI0aNcLx48fxwgsvoH379pg+fTqSkpKwYcMG1K9f39bhURHdSEo3ql4FVwcLR0JERFQ6mZyUylOjRg3UqFHDnLEQERER2aV58+Zp19ycPXs2hg4dijFjxqB69epYs2aNjaOjolCpBXwXU/goNz93B+68R0REVERGJaUmTJiA2bNnw9nZGRMmTCiw7qJFi8wSGBEREZG9CA4O1h6XL18eu3btsmE0ZFB6OhAYqDm+cQNwdi6wekxcChJTC5+WN7BpFe68R0REVERGJaVOnToFpVKpPSYiIiIisjtJSUZX3RubaFS9QG+nokZDRERU5hmVlPrzzz8NHhMRERGVVY0bN8Yff/wBDw8PNGrUSLvQuSEnT560YmRUXCq1gJ9O3zaqLteTIiIiKjqT15R688038cUXX8DV1VWnPD09He+++y5Wr15ttuCIiIiISqqePXtCoVAAAHr16mXbYMisYuJSkJKuLLSel7Oc60kREREVg8lJqXXr1uGTTz7RS0plZmZi/fr1TEoRERFRmTBjxgwAgEqlQrt27dCgQQN4eHjYOCoyh3uPs4yq1/PFilxPioiIqBiMTkqlpqZCEAQIgoDHjx/DweHpUGWVSoVdu3ahQoUKFgmSiIiIqKSSSCQICwvDhQsXmJQqJW4kpRtVr2MdXwtHQkREVLoZnZQqV64cRCIRRCIRXnjhBb3zIpEIM2fONGtwRERERPagfv36uH79OoKCgmwdChWTSi3gu5j4Quv5uTtw6h4REVExGZ2U+vPPPyEIAl5++WX88MMP8PR8+kdYLpcjICAAFStWtEiQRERERCXZ3LlzMWnSJMyePRtNmjSBs7Ozznk3NzcbRUZaYjEQHPz0OB8xcSlITM0u9HYDm1bh1D0iIqJiMjop1bZtWwBAXFwc/P39IS7gjzkRERFRWdKpUycAQI8ePXR24RMEASKRCCqVylahUR5HR+CffwqtZux6UoHeTsWNiIiIqMwzeaHzgIAAAEBGRgbi4+ORk5Ojc75BgwbmiYyIiIjITvz555+2DoHMxNj1pCq4OhReiYiIiApkclLq/v37GD58OH777TeD5/lNIBEREZU1eSPKyb5xPSkiIiLrMjkpNW7cODx48ABHjx5F+/bt8dNPP+Hu3buYM2cOPvvsM0vESERERGQXOJK8BMvIAOrU0RzHxgJO+tPvuJ4UERGRdZmclNq3bx9++eUXNG3aFGKxGAEBAejYsSPc3NwQGRmJrl27WiJOIiIiohKLI8ntgCAA//339NgAridFRERkXSavVp6eno4KFSoAADw9PXH//n0Amq2QT548ad7oiIiIiOzAsyPJHR0d8fvvv2PdunWoUaMGtm/fbuvwyEhcT4qIiMi6TB4pVbNmTVy6dAmBgYF48cUXsWzZMgQGBuKbb76Bn5+fJWIkIiIiKtE4ktz+cT0pIiIi6yvSmlIJCQkAgBkzZiAsLAwbN26EXC7H2rVrzR0fERERUYlnaCT5Cy+8wJHkdoTrSREREVmfyUmp119/XXvcqFEj3LhxAxcvXkSVKlXg7e1t1uCIiIiI7AFHkts/ridFRERkfSYlpZRKJWrWrIlff/0VdZ7sXuLk5ITGjRtbJDgiIiIie8CR5PaP60kRERFZn0lJKZlMhuzsbIhEHLJMRERE1KtXL4wcORKDBg2CWKzZP4YjyUsokQh48qUqnuvLcj0pIiIi2zB59713330X8+fPR25uriXiISIiIrIbmZmZ6NWrFypXroypU6fiypUrAJ6OJGdCqgRxcgLOn9e8nHSn4HE9KSIiItsweU2pY8eO4Y8//sCePXtQv359ODs765z/8ccfzRYcERERUUm2e/du3Lp1C2vWrMG6deswf/58tGrVCiNHjkS/fv3g6Oho6xDJCFxPioiIyDZMHilVrlw5vPrqqwgLC0PFihXh7u6u8yIiIiIqSypXroxp06bh6tWr2Lt3LwICAhAeHg5fX1+MGjUKx44dM/meUVFRCAoKgoODA5o0aYIDBw4UWH/JkiWoXbs2HB0dUbNmTaxfv17n/IoVK9C6dWt4eHjAw8MDr7zyCmJiYnTqfPzxxxCJRDovX19fk2O3R1xPioiIyDZMHim1Zs0aS8RBREREZPfat2+P9u3b4/Hjx9i0aROmTp2KVatWmbTswZYtWzBu3DhERUWhVatWWLZsGTp37ozY2FhUqVJFr/7SpUsRERGBFStWoGnTpoiJicFbb70FDw8PdO/eHQDw119/YdCgQWjZsiUcHBywYMEChIaG4vz586hUqZL2XnXr1sXevXu17yUSSTFao4TJyACaNtUc//OPdgof15MiIiKyHZOTUkRERESUv+vXr2Pt2rVYu3YtHj16hFdeecWk6xctWoQRI0Zg5MiRAIDFixdj9+7dWLp0KSIjI/Xqb9iwAaNGjcKAAQMAAFWrVsXRo0cxf/58bVJq48aNOtesWLEC33//Pf744w8MGTJEWy6VSkvv6ChBAGJjnx4/wfWkiIiIbIdJKSIiIqJiyszMxLZt27BmzRr8/fffqFKlCkaOHInhw4fD39/f6Pvk5OTgxIkTmDJlik55aGgoDh8+bPCa7OxsODjoTitzdHRETEwMlEolZDKZ3jUZGRlQKpXw9NQd+XPlyhVUrFgRCoUCzZs3x7x581C1atV8483OzkZ29tOETmpqKgBAqVRCqVQW/GFNkHevYt1TqYRMe6gEntwr4aFxU/f8PRRm/UwlkVnamQrFdrY8trF1sJ0tz57b2NiYmZQiIiIiKqLDhw9jzZo12Lp1K3JyctCrVy/s3r3b5NFReZKSkqBSqeDj46NT7uPjg8TERIPXhIWFYeXKlejVqxcaN26MEydOYPXq1VAqlUhKSoKfn5/eNVOmTEGlSpV04mzevDnWr1+PF154AXfv3sWcOXPQsmVLnD9/Hl5eXgafHRkZiZkzZ+qV79mzB05O5l8UPDo6usjXSrKy0O3J8e7du6F6ksi7/kgEoPBpitfPn8auW6eK/Hx7Upx2JuOxnS2PbWwdbGfLs8c2zsjIMKoek1JERERERfTSSy+hYcOGmDt3Ll5//XV4eHiY5b4ike40MUEQ9MryTJs2DYmJiWjRogUEQYCPjw+GDRuGBQsWGFwTasGCBfjuu+/w119/6Yyw6ty5s/a4fv36CAkJQbVq1bBu3TpMmDDB4LMjIiJ0zqWmpsLf3x+hoaFwc3Mz6TMXRKlUIjo6Gh07djQ48sso6U9HRIWFhQFPdpD+7d9EIPZsgZf6uSvwvwFtSv30PbO0MxWK7Wx5bGPrYDtbnj23cd7o6cIUKymVlZWlN1yciIiIqKw4fvw4GjdubLb7eXt7QyKR6I2Kunfvnt7oqTyOjo5YvXo1li1bhrt378LPzw/Lly+Hq6srvL29dep++umnmDdvHvbu3YsGDRoUGIuzszPq16+PK1eu5FtHoVBAoVDolctkMot0not132euk8lkgEwGlVpA5O7LhV46rWtdOCjkRXuuHbLUz490sZ0tj21sHWxny7PHNjY2XrGpN1ar1Zg9ezYqVaoEFxcXXL9+HYDmW7pVq1aZejsiIiIiu2XOhBQAyOVyNGnSRG+YfnR0NFq2bFngtTKZDJUrV4ZEIsHmzZvRrVs3iMVPu3oLFy7E7Nmz8fvvvyM4OLjQWLKzs3HhwgWD0/9Ki5i4FCQ8yiq0nodz2UlIERERWZPJSak5c+Zg7dq1WLBgAeTyp3+g69evj5UrV5o1OCIiIqKyZsKECVi5ciVWr16NCxcuYPz48YiPj8fo0aMBaKbMPbtj3uXLl/Htt9/iypUriImJwcCBA3Hu3DnMmzdPW2fBggX46KOPsHr1agQGBiIxMRGJiYlIS0vT1pk0aRL279+PuLg4HDt2DH379kVqaiqGDh1qvQ9vSSIREBCgeT2ZCnnvceEJKVPqERERkWlMnr63fv16LF++HB06dNB2jgCgQYMGuHjxolmDIyIiIiprBgwYgOTkZMyaNQsJCQmoV68edu3ahYCAAABAQkIC4uPjtfVVKhU+++wzXLp0CTKZDO3bt8fhw4cRGBiorRMVFYWcnBz07dtX51kzZszAxx9/DAC4desWBg0ahKSkJJQvXx4tWrTA0aNHtc+1e05OwI0bOkUVXI1bhsLYekRERGQak5NSt2/fRvXq1fXK1Wq1XW5TSERERFTShIeHIzw83OC5tWvX6ryvXbs2Tp0qeFe4G88lYwzZvHmzseGVGg/Sswut4+fugGZBnlaIhoiIqOwxefpe3bp1ceDAAb3ybdu2oVGjRmYJioiIiIjIklRqAbN3Xii03rSudUr9rntERES2YvJIqRkzZmDw4MG4ffs21Go1fvzxR1y6dAnr16/Hr7/+aokYiYiIiEqcRo0aQSQyLllx8uRJC0dDhcrMBNq00Rz//Tdi7mRwkXMiIiIbM3mkVPfu3bFlyxbs2rULIpEI06dPx4ULF7Bjxw507NjR5ACioqIQFBQEBwcHNGnSxOAorGdlZ2fjww8/REBAABQKBapVq4bVq1eb/FwiIiKi4ujVqxd69uyJnj17IiwsDNeuXYNCoUC7du3Qrl07ODg44Nq1awgLC7N1qAQAajVw/LjmpVZzkXMiIqISwOSRUgAQFhZmlg7Wli1bMG7cOERFRaFVq1ZYtmwZOnfujNjYWFSpUsXgNf3798fdu3exatUqVK9eHffu3UNubm6xYyEiIiIyxYwZM7THI0eOxNixYzF79my9Ojdv3rR2aGQELnJORERkeyYnpf755x+o1Wo0b95cp/zYsWOQSCQIDg42+l6LFi3CiBEjMHLkSADA4sWLsXv3bixduhSRkZF69X///Xfs378f169fh6enZsHJZ3eWISIiIrKFbdu24fjx43rlb7zxBoKDgzmquwRqFuSJck4yPMzIf6MeDycZFzknIiKyIJOTUu+88w7ef/99vaTU7du3MX/+fBw7dsyo++Tk5ODEiROYMmWKTnloaCgOHz5s8Jrt27cjODgYCxYswIYNG+Ds7IwePXpg9uzZcHR0NHhNdnY2srOf7qySmpoKAFAqlWbfLTDvfiJBxZ0ILSivbdnGlsM2tg62s+Wxja3DntvZXDE7Ojri4MGDqFGjhk75wYMH4eDAkTb2SrB1AERERKWcyUmp2NhYNG7cWK+8UaNGiI2NNfo+SUlJUKlU8PHx0Sn38fFBYmKiwWuuX7+u7dz99NNPSEpKQnh4OFJSUvL9BjIyMhIzZ87UK9+zZw+cnJyMjtcUgVnXsGvXNYvcm56Kjo62dQilHtvYOtjOlsc2tg57bOeMjAyz3GfcuHEYM2YMTpw4gRYtWgAAjh49itWrV2P69OlmeQaZV0xcSoGjpADgYYYSMXEpCKnmZaWoiIiIyhaTk1IKhQJ3795F1apVdcoTEhIglZq+RNXzu9YIgpDvTjZqtRoikQgbN26Eu7s7AM0UwL59+2LJkiUGR0tFRERgwoQJ2vepqanw9/dHaGgo3NzcTI63IEqlEtHR0bjhUA3hL9c0673pqbx27tixI2Qyma3DKZXYxtbBdrY8trF12HM7542gLq4pU6agatWq+OKLL7Bp0yYAQO3atbF27Vr079/fLM8g8+JC50RERLZnchapY8eOiIiIwC+//KJNDD18+BBTp041afc9b29vSCQSvVFR9+7d0xs9lcfPzw+VKlXSPhfQdPgEQcCtW7f0hswDmiSaQqHQK5fJZBbrOAsiid11yu2RJX+GpME2tg62s+Wxja3DHtvZnPH279+fCaiSzttbe8iFzomIiGxPbOoFn332GW7evImAgAC0b98e7du3R1BQEBITE/HZZ58ZfR+5XI4mTZroDfWPjo5Gy5YtDV7TqlUr3LlzB2lpadqyy5cvQywWo3LlyqZ+FCIiIiKzefjwIVauXImpU6ciJSUFAHDy5Encvn3bxpERAMDZGbh/X/NydsaD9OxCL/Fzd+BC50RERBZkclKqUqVKOHv2LBYsWIA6deqgSZMm+OKLL/Dvv//C39/fpHtNmDABK1euxOrVq3HhwgWMHz8e8fHxGD16NADN1LshQ4Zo67/22mvw8vLC8OHDERsbi7///huTJ0/Gm2++me9C50RERESWdvbsWbzwwguYP38+Fi5ciIcPHwIAfvrpJ0RERNg2ONKjUguYvfNCofWmda0DidjwshJERERUfKYvAgXA2dkZb7/9drEfPmDAACQnJ2PWrFlISEhAvXr1sGvXLgQEBADQrFMVHx+vre/i4oLo6Gi8++67CA4OhpeXF/r37485c+YUOxYiIiKiopowYQKGDRuGBQsWwNXVVVveuXNnvPbaazaMjAyJiUtBwqPC14rycJZbIRoiIqKyq0hJqcuXL+Ovv/7CvXv3oFardc6ZusNMeHg4wsPDDZ5bu3atXlmtWrXscncfIiIiKr3++ecfLFu2TK+8UqVK+e4qTFaWmQl07gwASPpkpVGXcJFzIiIiyzI5KbVixQqMGTMG3t7e8PX11dkpTyQScdtjIiIiKnMcHBwM7uR36dIllC9f3gYRkR61Gti/HwBQwcgRUFzknIiIyLJMTkrNmTMHc+fOxQcffGCJeIiIiIjsTs+ePTFr1ixs3boVgOaLuvj4eEyZMgWvvvqqjaOj5wUHeqKckwwPM5QGz4sA+HKRcyIiIoszeaHzBw8eoF+/fpaIhYiIiMguffrpp7h//z4qVKiAzMxMtG3bFtWrV4erqyvmzp1r6/DoOfsu3M03IQUAAoAZ3bnIORERkaWZPFKqX79+2LNnj3aHPCIiIqKyzs3NDQcPHsS+fftw8uRJqNVqNG7cGK+88oqtQyMD5v12AZrxUIaVc5KhYx1f6wVERERURpmclKpevTqmTZuGo0ePon79+pDJZDrnx44da7bgiIiIiOzJyy+/jJdfftnWYVAhEh9lA/L814t6mKFETFwKQqp5WTEqIiKissfkpNTy5cvh4uKC/fv3Y/+TxSLziEQiJqWIiIioTPrjjz/wxx9/GNydePXq1TaKioqKO+8RERFZnslJqbi4OEvEQURERGS3Zs6ciVmzZiE4OBh+fn46uxNTCeLkBJVgXFXuvEdERGR5JieliIiIiEjXN998g7Vr12Lw4MG2DoXy4+wMpKdj99k7yNx0qsCqftx5j4iIyCqKlJS6desWtm/fjvj4eOTk5OicW7RokVkCIyIiIrIXOTk5aNmypa3DoEKo1AJm77xQaL1pXbnzHhERkTWYnJT6448/0KNHDwQFBeHSpUuoV68ebty4AUEQ0LhxY0vESERERFSijRw5Eps2bcK0adNsHQoVICYuBQmPCl8rysNZboVoiIiIyOSkVEREBCZOnIhZs2bB1dUVP/zwAypUqIDXX38dnTp1skSMRERERCVaVlYWli9fjr1796JBgwZ6uxNzJHkJkJWFoGH9sfpOKsb0nopsaf6JJy5yTkREZB0mJ6UuXLiA7777TnOxVIrMzEy4uLhg1qxZ6NmzJ8aMGWP2IImIiIhKsrNnz+LFF18EAJw7d07nHBc9LyFUKvge3AdfAOLndkd8Hhc5JyIisg6Tk1LOzs7Izs4GAFSsWBHXrl1D3bp1AQBJSUnmjY6IiIjIDvz555+2DoHMQATAl4ucExERWY3JSakWLVrg0KFDqFOnDrp27YqJEyfi33//xY8//ogWLVpYIkYiIiIiIosTAMzozkXOiYiIrMXkpNSiRYuQlpYGAPj444+RlpaGLVu2oHr16vj888/NHiARERFRSdSnTx+sXbsWbm5u6NOnT4F1f/zxRytFRflRqQVICqlTzkmGjnV8rRIPERERFSEpVbVqVe2xk5MToqKizBoQERERkT1wd3fXrhfl7u5u42ioMMdvpKB5IXUeZigRE5eCkGpeVomJiIiorDM5KUVEREREwJo1awweU8l0Py3bqHrceY+IiMh6jEpKeXp64vLly/D29oaHh0eBu8ikpKSYLTgiIiIiInMo76Iwqh533iMiIrIeo5JSn3/+OVxdXQEAixcvtmQ8RERERHbp+++/x9atWxEfH4+cnBydcydPnrRRVJQnuK4/QubtReKjLAgGznPnPSIiIuszKik1dOhQAEBubi4AICwsDL6+XASSiIiICAC+/PJLfPjhhxg6dCh++eUXDB8+HNeuXcM///yDd955x9bhEQCJWIQeDf2w7O+4fOtw5z0iIiLrEptSWSqVYsyYMcjONm5OPhEREVFZEBUVheXLl+Prr7+GXC7H+++/j+joaIwdOxaPHj2ydXgE4PdzCVheQELq7TZB6FTPz4oRERERkUlJKQBo3rw5Tp06ZYlYiIiIiOxSfHw8WrZsCQBwdHTE48ePAQCDBw/Gd999Z8vQCIBKLSDyx9P4+udILPk5EorcHL06288kQKU2NLGPiIiILMXk3ffCw8MxceJE3Lp1C02aNIGzs7PO+QYNGpgtOCIiIiJ74Ovri+TkZAQEBCAgIABHjx5Fw4YNERcXB0FgosPWYuJScO9hBrpeOgQAmNRlvF6dhEdZiIlLQUg1L2uHR0REVGaZnJQaMGAAAGDs2LHaMpFIBEEQIBKJoFKpzBcdERERkR14+eWXsWPHDjRu3BgjRozA+PHj8f333+P48ePo06ePrcMr8+49zjJrPSIiIjIPk5NScXH5z8UnIiIiKouWL18OtVoNABg9ejQ8PT1x8OBBdO/eHaNHj7ZxdFTB1cGs9YiIiMg8TE5KBQQEWCIOIiIiIrslFoshFj9dqrN///7o37+/DSOiZzUL8oS7kyzf8yIAvu4OaBbkab2giIiIyPSkVJ7Y2FjEx8cjJ0d3ocgePXoUOygiIiKiku7s2bNG1+Wam7YVHZuIRxnKfM8LAGZ0rwOJWGS9oIiIiMj0pNT169fRu3dv/Pvvv9q1pADNulIAuKYUERERlQkvvviiTl8oP1xz07ZUagEzd8QWWKeckwwd6/haKSIiIiLKY3JS6r333kNQUBD27t2LqlWrIiYmBsnJyZg4cSI+/fRTS8RIREREVOJwnU37EBOXgoRHWXAsoM7DDCV33iMiIrIBk5NSR44cwb59+1C+fHnt+gkvvfQSIiMjMXbsWJw6dcoScRIRERGVKFxn0z7k7aiXKVOg9vjvtcf51SMiIiLrMTkppVKp4OLiAgDw9vbGnTt3ULNmTQQEBODSpUtmD5CIiIjIHly6dAlfffUVLly4AJFIhFq1auHdd99FzZo1bR1amabdUU8kQqY8/931uPMeERGR9YkLr6KrXr162oU9mzdvjgULFuDQoUOYNWsWqlatavYAiYiIiEq677//HvXq1cOJEyfQsGFDNGjQACdPnkS9evWwbds2W4dXpjUL8kS5Qnbe8+POe0RERDZh8kipjz76COnp6QCAOXPmoFu3bmjdujW8vLywZcsWswdIREREVNK9//77iIiIwKxZs3TKZ8yYgQ8++AD9+vWzUWQUHZuIhxlKyHOVmLf7awDA1LD/IUeqSVRx5z0iIiLbMTkpFRYWpj2uWrUqYmNjkZKSAg8PD+0OfERERERlSWJiIoYMGaJX/sYbb2DhwoU2iIgA3Z33JGoV+p77AwAwreMYAJqkFHfeIyIish2Tp++tW7dOO1Iqj6enJxNSREREVGa1a9cOBw4c0Cs/ePAgWrdubYOICHi6815B8nbeIyIiIuszeaTUpEmTEB4eju7du+ONN95Ap06dIJWafBsiIiKiUqNHjx744IMPcOLECbRo0QIAcPToUWzbtg0zZ87E9u3bdeqSdRi7ox533iMiIrINk0dKJSQkYMuWLZBIJBg4cCD8/PwQHh6Ow4cPWyI+IiIiohIvPDwcSUlJiIqKwpAhQzBkyBBERUXh/v37CA8PR69evdCrVy/07t3bqPtFRUUhKCgIDg4OaNKkicFRWM9asmQJateuDUdHR9SsWRPr16/Xq/PDDz+gTp06UCgUqFOnDn766adiP7ekM3ZHPe68R0REZBsmJ6WkUim6deuGjRs34t69e1i8eDH+++8/tG/fHtWqVbNEjEREREQlmlqtNuqlUqkKvdeWLVswbtw4fPjhhzh16hRat26Nzp07Iz4+3mD9pUuXIiIiAh9//DHOnz+PmTNn4p133sGOHTu0dY4cOYIBAwZg8ODBOHPmDAYPHoz+/fvj2LFjRX6uPWgW5Ak/dwfkt8gEd94jIiKyLZOTUs9ycnJCWFgYOnfujBo1auDGjRtmCouIiIiodMjIyDCp/qJFizBixAiMHDkStWvXxuLFi+Hv74+lS5carL9hwwaMGjUKAwYMQNWqVTFw4ECMGDEC8+fP19ZZvHgxOnbsiIiICNSqVQsRERHo0KEDFi9eXOTn2gOJWIQeDf0gFFCHO+8RERHZTpEWg8rIyMBPP/2EjRs3Yu/evfD398egQYOwbds2c8dHREREVOK1a9cO3377LSpXrqxTfuzYMQwePBiXL1826j45OTk4ceIEpkyZolMeGhqa71IJ2dnZcHDQnX7m6OiImJgYKJVKyGQyHDlyBOPHj9epExYWpk1KFeW5ec/Ozs7Wvk9NTQUAKJVKKJXKgj+sCfLuZeo9d5+/i+V/x+V7fkSrAHSo6W3WWO1ZUduZTMN2tjy2sXWwnS3PntvY2JhNTkoNGjQIO3bsgJOTE/r164e//voLLVu2NDlAIiIiotLCzc0NDRo0QFRUFAYOHAi1Wo1Zs2YhMjIS7777rtH3SUpKgkqlgo+Pj065j48PEhMTDV4TFhaGlStXolevXmjcuDFOnDiB1atXQ6lUIikpCX5+fkhMTCzwnkV5LgBERkZi5syZeuV79uyBk5OTUZ/ZFNHR0UbXVQvAzJOSJ6OkRMiUKdD43Y0AgEyZAoCA7/+5gbqqa+BAKV2mtDMVHdvZ8tjG1sF2tjx7bGNjR4qbnJQSiUTYsmULwsLCuOseEREREYDt27fjm2++wciRI7F9+3bcuHED8fHx2LlzJ1555RWT7ycS6WZJBEHQK8szbdo0JCYmokWLFhAEAT4+Phg2bBgWLFgAiURi0j1NeS4AREREYMKECdr3qamp8Pf3R2hoKNzc3Ar+kCZQKpWIjo5Gx44dIZPJjLrmWFwKHh49/rRAJEKKk7tOnYc5QPk6LdCca0oBKFo7k+nYzpbHNrYOtrPl2XMb542eLozJWaVNmzaZHAwRERFRaTd69Gj8999/mD9/PqRSaZFGk3t7e0MikeiNTrp3757eKKY8jo6OWL16NZYtW4a7d+/Cz88Py5cvh6urK7y9vQEAvr6+Bd6zKM8FAIVCAYVCoVcuk8ks0nk25b7JGblG17O3jr6lWernR7rYzpbHNrYOtrPl2WMbGxtvsRY6JyIiIiLgwYMHePXVV7F06VIsW7YM/fv3R2hoKKKioky6j1wuR5MmTfSG6UdHRxea4JLJZKhcuTIkEgk2b96Mbt26QSzWdPVCQkL07rlnzx7tPYvz3JKqgqvuOlvyXCVm7VmKWXuWQp6rzLceERERWQ/n3xEREREVU7169RAUFIRTp04hKCgIb731FrZs2YLw8HDs3LkTO3fuNPpeEyZMwODBgxEcHIyQkBAsX74c8fHxGD16NADNlLnbt29j/fr1AIDLly8jJiYGzZs3x4MHD7Bo0SKcO3cO69at097zvffeQ5s2bTB//nz07NkTv/zyC/bu3YuDBw8a/Vx70yzIE37uDkh8lAUBgEStwpBTmp9DZLvhEEEGX3cHNOPUPSIiIpthUoqIiIiomEaPHo0PP/xQOzIJAAYMGIBWrVph+PDhJt1rwIABSE5OxqxZs5CQkIB69eph165dCAgIAAAkJCQgPj5eW1+lUuGzzz7DpUuXIJPJ0L59exw+fBiBgYHaOi1btsTmzZvx0UcfYdq0aahWrRq2bNmC5s2bG/1ceyMRizCjex2M+fYknl8VK+/9jO51IOEq50RERDbDpBQRERFRMU2bNs1geeXKlYu0Y054eDjCw8MNnlu7dq3O+9q1a+PUqVOF3rNv377o27dvkZ9rjzrV88PbbYKw4kCcTrlIBLzdJgid6vnZKDIiIiICjFxTKjU11egXERERUVmxYMECZGZmat///fffyM7O1r5//PhxqUry2JvfzyVg+d9xUAu65WoBWP53HH4/l2CbwIiIiAiAkUmpcuXKwcPDw6gXERERUVkRERGBx48fa99369YNt2/f1r7PyMjAsmXLbBFamadSC5i5IxZCAXVm7oiF6vmMFREREVmNUdP3/vzzT+3xjRs3MGXKFAwbNgwhISEAgCNHjmDdunWIjIy0TJREREREJZAgCAW+J9uJiUtBwqOsfM8LABIeZSEmLgUh1bysFxgRERFpGZWUatu2rfZ41qxZWLRoEQYNGqQt69GjB+rXr4/ly5dj6NCh5o+SiIiIiMgE9x7nn5AqSj0iIiIyP6Om7z3ryJEjCA4O1isPDg5GTEyMWYIiIiIiIiqOCq4OOu+zZHK8NHoVXhq9Clkyeb71iIiIyHpM3n3P398f33zzDT777DOd8mXLlsHf399sgRERERHZg5UrV8LFxQUAkJubi7Vr18Lb2xsAdNabIutqFuQJP3cHJD7KggBAEIlxy91He14EwNfdAc2CPG0WIxERUVlnclLq888/x6uvvordu3ejRYsWAICjR4/i2rVr+OGHH8weIBEREVFJVaVKFaxYsUL73tfXFxs2bNCrQ9YnEYswo3sdjPn2pN450ZN/zuheBxKxSO88ERERWYfJSakuXbrg8uXLWLp0KS5evAhBENCzZ0+MHj2aI6WIiIioTLlx44atQ6BCuDlI8SgrFzKVEpP+1iQMV4a9idn9G6NTPT8bR0dERFS2mZyUAjRT+ObNm2fuWIiIiIiIzOL3cwkY8+1J5O2HKFWpMCrmRwDA4lav2S4wIiIi0jJ5oXMAOHDgAN544w20bNkSt2/fBgBs2LABBw8eNGtwRERERESmUqkFzNwRq01IPU8EYOaOWKjU+dUgIiIiazA5KfXDDz8gLCwMjo6OOHnyJLKzswFoFvLk6CkiIiIisrWYuBQkPMrK97wAIOFRFmLiUqwXFBEREekxOSk1Z84cfPPNN1ixYgVkMpm2vGXLljh5Un8hSSIiIiIia7r3OP+EVFHqERERkWWYnJS6dOkS2rRpo1fu5uaGhw8fmiMmIiIiIqIiq+DqYNZ6REREZBkmJ6X8/Pxw9epVvfKDBw+iatWqZgmKiIiIyN5cu3YNH330EQYNGoR79+4BAH7//XecP3/expGVPc2CPOHn7gBRPudFAPzcHdAsyNOaYREREdFzTE5KjRo1Cu+99x6OHTsGkUiEO3fuYOPGjZg0aRLCw8MtESMRERFRibZ//37Ur18fx44dw48//oi0tDQAwNmzZzFjxgwbR1f2SMQizOhep8A6M7rXgUScX9qKiIiIrEFq6gXvv/8+Hj16hPbt2yMrKwtt2rSBQqHApEmT8L///c8SMRIRERGVaFOmTMGcOXMwYcIEuLq6asvbt2+PL774woaRlW3uTjI8zFACALJkcnR8cwncHKVYNKwFOtXzs3F0REREZHJSCgDmzp2LDz/8ELGxsVCr1ahTpw5cXFzMHRsRERGRXfj333+xadMmvfLy5csjOTnZBhGVbb+fS8CYb09CeKZMEIlxpXwAAOAtscmTBYiIiMgCivwX2cnJCcHBwWjWrBkTUkRERFSmlStXDgkJCXrlp06dQqVKlWwQUdmlUguYuSNWJyH1LBGAmTtioVLnV4OIiIisxeSRUunp6fjkk0/wxx9/4N69e1Cr1Trnr1+/brbgiIiIiOzBa6+9hg8++ADbtm2DSCSCWq3GoUOHMGnSJAwZMsTW4ZUpMXEpSHiUpVcuUynxzpGtAIAlIf0RE5eCkGpe1g6PiIiInmFyUmrkyJHYv38/Bg8eDD8/P4hEXCCSiIiIyra5c+di2LBhqFSpEgRBQJ06daBSqfDaa6/ho48+snV4Zcq9x/oJKQCQqlQYd+g7AMCyZq/mW4+IiIisx+Sk1G+//YadO3eiVatWloiHiIiIyO7IZDJs3LgRs2bNwqlTp6BWq9GoUSPUqFHD1qGVORVcHcxaj4iIiCzH5KSUh4cHPD09LRELERERkV3av38/2rZti2rVqqFatWq2DqdMaxbkCT93ByQ+ysp3XSlfdwWaBbE/S0REZGsmL3Q+e/ZsTJ8+HRkZGZaIh4iIiMjudOzYEVWqVMGUKVNw7tw5W4dTpknEIszoXqfAOlM714ZEzCUoiIiIbM3kpNRnn32G3bt3w8fHB/Xr10fjxo11XkRERERlzZ07d/D+++/jwIEDaNCgARo0aIAFCxbg1q1btg6tTOpUzw9LXmsMaT6Jp451fa0cERERERli8vS9Xr16WSAMIiIiIvvl7e2N//3vf/jf//6HuLg4bNq0CevXr8fUqVPRpk0b7Nu3z9Yhlim/n0vA7J2xyFU/ncDn4SyzYURERERkiMlJqRkzZlgiDiIiIqJSISgoCFOmTEHDhg0xbdo07N+/39YhlSm/n0vAmG9P6q0n9TBdaZN4iIiIKH8mJ6WIiIiIyLBDhw5h48aN+P7775GVlYUePXpg3rx5tg6rzFCpBczcEWtwgfMsqQw9hyyCl4scK+QKSKweHRERET3PqKSUp6cnLl++DG9vb3h4eEAkyn9hyJSUFLMFR0RERGQPpk6diu+++w537tzBK6+8gsWLF6NXr15wcnKydWhlSkxcChIeZRk8pxZLcMbvBU29+EcIqeZlzdCIiIjIAKOSUp9//jlcXV0BAIsXL7ZkPERERER256+//sKkSZMwYMAAeHt72zqcMuveY8MJqaLWIyIiIssyKik1dOhQg8dEREREBBw+fNjWIRCACq4O+Z6TqZQYfnw7AMBnKHeMJiIiKgmKtaZUZmYmlErdRSPd3NyKFRARERGRPdi+fTs6d+4MmUyG7du3F1i3R48eVoqqbGsW5Ak/dwckPsrSW1dKqlJh6l9rAACqyl9aPzgiIiLSY3JSKj09HR988AG2bt2K5ORkvfMqlcosgRERERGVZL169UJiYiIqVKiAXr165VtPJBKxf2QlErEIM7rXwZhvT+qdEz1Xj4iIiGxPbOoF77//Pvbt24eoqCgoFAqsXLkSM2fORMWKFbF+/XpLxEhERERU4qjValSoUEF7nN+LCSnr6lTPD0teawy5RDfx5OOusFFERERElB+Tk1I7duxAVFQU+vbtC6lUitatW+Ojjz7CvHnzsHHjRkvESERERFSirV+/HtnZ2XrlOTk5/NLOyn4/l4DZO2ORo3o6gc/TWYYpYbVsGBUREREZYnJSKiUlBUFBQQA060elpKQAAF566SX8/fff5o2OiIiIyA4MHz4cjx490it//Pgxhg8fboOIyqbfzyVgzLcnkfBId3e9B+lKjN96xkZRERERUX5MTkpVrVoVN27cAADUqVMHW7duBaAZQVWuXDlzxkZERERkFwRBgEikv07RrVu34O7uboOIyh6VWsDMHbF6C5wDMFhGREREtmfyQufDhw/HmTNn0LZtW0RERKBr16746quvkJubi0WLFlkiRiIiIqISqVGjRhCJRBCJROjQoQOk0qddK5VKhbi4OHTq1MmGEZYdMXEpeiOknsXEFBERUcljclJq/Pjx2uP27dvj4sWLOH78OKpVq4aGDRuaNTgiIiKikixv173Tp08jLCwMLi4u2nNyuRyBgYF49dVXbRRd2XLvcf4JKQDIlsowcNA8/O/l6njJwcFKUREREVFBTE5KPa9KlSqoUqVKka+PiorCwoULkZCQgLp162Lx4sVo3bp1odcdOnQIbdu2Rb169XD69OkiP5+IiIioqGbMmAEACAwMxIABA+DAZIfNVHAtuO3VYgmOVmmA99q3ACQSK0VFREREBTEqKfXll18afcOxY8caXXfLli0YN24coqKi0KpVKyxbtgydO3dGbGxsgYmuR48eYciQIejQoQPu3r1r9POIiIiILGHo0KG2DqHMaxbkCT93ByQ+yjI4VU8EwNfdAc2CPK0dGhEREeXDqKTU559/btTNRCKRSUmpRf9v777jmrreP4B/QhhhK4gMRcAt4kIc4KwDXLh+1oWDOr5V3DiqVQtYZ+usrdRaR1sH2jpaK0XRurc4qoLWgcXaIBUVRGTm/v6gRGMCBiQLP+/XK99vcu+59z55QpvTJ+ecu3w5Ro4ciVGjRgEAVq5cif379yMyMhKLFi0q8rgPP/wQgwcPhlgsxp49e9S+HhEREZEm5OfnY8WKFdixYweSkpKQk5OjsL/wbsWkOWIjEcICPTF280WlfSIAxvl5WJ9xFuLIG8D//geYmGg/SCIiIlKg1t33EhMT1XrcvXtX7Qvn5OQgLi4O/v7+Ctv9/f1x6tSpIo/buHEj7ty5Ix8uT0RERKRrERERWL58Ofr374+0tDSEhoaib9++MDIyQnh4uK7De2d08XLGV4O9ITFR7OI62Uqw5n0veC6cDYwfD7xWNCQiIiLdeKs1pQShYHC0qlsgv8mjR4+Qn58PR0dHhe2Ojo5ITk5WecytW7cwc+ZMHD9+XOHuNsXJzs5Gdna2/HV6ejoAIDc3F7m5uSWOuziF5xMJ+WV+bnqpMLfMseYwx9rBPGsec6wdhpznsop5y5YtWLduHbp3746IiAgMGjQINWrUQMOGDXHmzJkSjSSn0ou5JsWn++KRlSuTb7OzNMHc7vXQuYatDiMjIiIiVUpVlFq/fj1WrFiBW7duAQBq1aqFyZMny6fhlcTrBS1BEFQWufLz8zF48GBERESgdu3aap9/0aJFiIiIUNp+4MABWFhYlDhedbhn3UF09B2NnJteio2N1XUI5R5zrB3Ms+Yxx9phiHnOzMwsk/MkJyejQYMGAAArKyukpaUBAHr06IG5c+eWyTWoeDHXpBi7+aLSelJPnudi3NZL+KZvXXTWSWRERERUlBIXpebOnYsVK1ZgwoQJ8PX1BQCcPn0aU6ZMwb179zB//ny1zlOpUiWIxWKlUVEpKSlKo6cA4NmzZ7hw4QIuXbqE8ePHAwBkMhkEQYCxsTEOHDiADh06KB03a9YshIaGyl+np6fD1dUV/v7+sLGxUft9qyM3NxexsbG4J6mBkA51yvTc9FJhnjt37gwTrgehEcyxdjDPmscca4ch57lwBPXbqlq1KqRSKapVq4aaNWviwIED8Pb2xvnz52FmZlYm16Ci5csEROyNV7nAuYCCNaUW/pbAohQREZGeKXFRKjIyEuvWrcOgQYPk23r27ImGDRtiwoQJahelTE1N0bRpU8TGxqJPnz7y7bGxsejVq5dSexsbG1y9elVh25o1a/D777/jp59+goeHh8rrmJmZqewMmpiYaKzjLIjEBtcpN0Sa/AypAHOsHcyz5jHH2mGIeS6rePv06YNDhw6hRYsWmDRpEgYNGoT169cjKSkJU6ZMKZNrUNHOJT6GNC2ryP0CgOS07CL3ExERkW6UuCiVn58PHx8fpe1NmzZFXl5eic4VGhqKoUOHwsfHB76+vvjmm2+QlJSEMWPGACgY5fTgwQN8//33MDIygpeXl8LxlStXhkQiUdpOREREpE2LFy+WP+/Xrx+qVq2KU6dOoWbNmujZs6cOI3s3pDwruiBFRERE+qvERakhQ4YgMjISy5cvV9j+zTffICgoqETnGjBgAFJTUzFv3jxIpVJ4eXkhOjoabm5uAACpVIqkpKSShkhERESkUy1btkTLli11HcY7o7K1RNchEBERUSmUeqHzAwcOyDtbZ86cwf379zFs2DCF9ZteL1ypEhISgpCQEJX7Nm3aVOyx4eHhvM0yERER6cQvv/yidluOltKs5h52cLaVIDktS+W6UiIA9nbWyP9lL8RGIoDrfBEREemFEhelrl27Bm9vbwDAnTsFd5hzcHCAg4MDrl27Jm+n6g56REREROVF79691WonEomQn59fonOvWbMGn3/+OaRSKerXr4+VK1eiTZs2RbbfsmULPvvsM9y6dQu2trbo0qULli5dCnt7ewBA+/btcfToUaXjunXrhn379gEo+LHv9TsWOzo6Kt2URh+JjUQIC/TE2M0XlfYV9kjn9G4AsZezdgMjIiKiYpW4KHX48GFNxEFERERkUGQymUbOu337dkyePBlr1qxBq1atsHbtWnTt2hXx8fGoVq2aUvsTJ05g2LBhWLFiBQIDA/HgwQOMGTMGo0aNwu7duwEAu3btQk5OjvyY1NRUNGrUCO+//77CuerXr4+DBw/KX4vFYo28R03o4uWMyCHe+GjnVaS9yJVvd7KVICzQE11YkCIiItI7RiU94OHDh0Xu++OPP94qGCIiIqJ33fLlyzFy5EiMGjUK9erVw8qVK+Hq6orIyEiV7c+cOQN3d3dMnDgRHh4eaN26NT788ENcuHBB3sbOzg5OTk7yR2xsLCwsLJSKUsbGxgrtHBwcNPpey1pnTyd09XIEANRxtMaWUS1w4qMOBQWp3Fxg06aCR25usechIiIi7SjxSKkGDRrg22+/VVobYenSpZg7dy5evHhRZsERERERGYJ58+YVu/+TTz5R6zw5OTmIi4vDzJkzFbb7+/vj1KlTKo/x8/PD7NmzER0dja5duyIlJQU//fQTunfvXuR11q9fj4EDB8LS0lJh+61bt+Di4gIzMzO0aNECCxcuRPXq1dWKXddirkkRsTce0rSCO/HdfPgM03688nKUVE4O8MEHBY3ffx8wMdFhtERERASUoij10UcfYcCAARg+fDhWrFiBx48fY+jQobh+/Tq2b9+uiRiJiIiI9FrhNLlCubm5SExMhLGxMWrUqKF2UerRo0fIz8+Ho6Ojwvbi1nby8/PDli1bMGDAAGRlZSEvLw89e/bE6tWrVbY/d+4crl27hvXr1ytsb9GiBb7//nvUrl0bDx8+xPz58+Hn54fr16/L16Z6XXZ2NrKzs+Wv09PT5e8/twxHIxWeq6hz7r/+EBOirigtcp6cloWxmy9i9cBGCHC3QmEZKjc3l6OlVHhTnqlsMM+axxxrB/OseYacY3VjLnFRaurUqejUqROGDBmChg0b4vHjx2jZsiX++OMPpQ4UERER0bvg0qVLStvS09MRHByMPn36lPh8r98wRhCEIm8iEx8fj4kTJ+KTTz5BQEAApFIppk+fjjFjxigVnoCCUVJeXl5o3ry5wvauXbvKnzdo0AC+vr6oUaMGvvvuO4W7K79q0aJFSoujA8CBAwdgYWHxxvdZUrGxsUrbZAIQcVH8X0Hqtbz9979zdl2GzPM5Csf579+/H/kSSZnHV16oyjOVPeZZ85hj7WCeNc8Qc5yZmalWuxIXpQCgevXqqF+/Pnbu3AkA6N+/PwtSRERERK+wsbHBvHnz0KNHDwwdOlStYypVqgSxWKw0KiolJaXIvtaiRYvQqlUrTJ8+HQDQsGFDWFpaok2bNpg/fz6cnV8u8J2ZmYmoqKg3TjcEAEtLSzRo0AC3bt0qss2sWbMUClbp6elwdXWFv78/bGxs3ngNdeXm5iI2NhadO3eGyWvT7s4mPsbTMxeKOBIARHiaA1Sq20y+JSAgAHht6iIVn2cqO8yz5jHH2sE8a54h57hw9PSblLgodfLkSQwZMgT29vb4448/cPLkSUyYMAH79u3D2rVrUbFixRIHS0RERFQePX36FGlpaWq3NzU1RdOmTREbG6swwio2Nha9evVSeUxmZiaMjRW7dIV3zRMExQltO3bsQHZ2NoYMGfLGWLKzs5GQkIA2bdoU2cbMzAxmZmZK201MTDTSeVZ13tTMPLWOffwiX+E8XFOqaJr6/EgR86x5zLF2MM+aZ4g5VjfeEhelOnTogClTpuDTTz+FiYkJ6tWrh/feew9Dhw5FgwYN8Pfff5c4WCIiIiJD9sUXXyi8FgQBUqkUP/zwA7p06VKic4WGhmLo0KHw8fGBr68vvvnmGyQlJWHMmDEACkYnPXjwAN9//z0AIDAwEKNHj0ZkZKR8+t7kyZPRvHlzuLi4KJx7/fr16N27t8o1oqZNm4bAwEBUq1YNKSkpmD9/PtLT0zF8+PASxa9tla3Vm4bnYKVcPCMiIiLdKnFR6sCBA2jXrp3Ctho1auDEiRNYsGBBmQVGREREZChWrFih8NrIyAgODg4YPnw4Zs2aVaJzDRgwAKmpqZg3bx6kUim8vLwQHR0NNzc3AIBUKkVSUpK8fXBwMJ49e4Yvv/wSU6dORYUKFdChQwcsWbJE4bx//vknTpw4gQMHDqi87t9//41Bgwbh0aNHcHBwQMuWLXHmzBn5dfVVcw87ONtKkJyWpbTQOVCwypSTrQQ+7nbaDo2IiIjeoMRFqdcLUoWMjIwwd+7ctw6IiIiIyNAkJiaW6flCQkIQEhKict+mTZuUtk2YMAETJkwo9py1a9dWms73qqioqBLFqC/ERiKEBXpi7OaLSvsKlz0PC/SE2FwC7NhRsEHFlEMiIiLSPiN1G3br1k1hTYQFCxbg6dOn8tepqanw9PQs0+CIiIiIiN6ki5czIod4o6KF4voVTrYSRA7xRhcvZ8DYGHj//YKHcanu9UNERERlTO1v5P379yM7O1v+esmSJRg0aBAqVKgAAMjLy8PNmzfLPEAiIiIifZeVlYXVq1fj8OHDSElJgUwmU9h/8aLyKB4qW509nRD31xOsO56IanbmWNS3IVpWt4fYSPTmg4mIiEgn1C5KvT7cu7jh30RERETvkhEjRiA2Nhb9+vVD8+bNIRKxEKJNMdekiNgbD2laFgAg6fELTPvxCsICPQtGSQFAXh6we3fB8z59OFqKiIhID/DbmIiIiOgt7du3D9HR0WjVqpWuQ3nnxFyTYuzmi0qLnCenZWHs5osvp+9lZwP9+xfszMhgUYqIiEgPqL2mlEgkUvrVj78CEhEREQFVqlSBtbW1rsN45+TLBETsjVd5173CbRF745Ev4wh/IiIifVSi6XvBwcEw++9uJVlZWRgzZgwsLS0BQGG9KSIiIqJ3ybJly/DRRx/h66+/hpubm67DeWecS3wsn7KnigBAmpaFc4mP4esk0V5gREREpBa1i1LDhw9XeD1kyBClNsOGDXv7iIiIiIgMjI+PD7KyslC9enVYWFjAxETxLnCPHz/WUWTlW8qzogtSSu1YlCIiItI7ahelNm7cqMk4iIiIiAzWoEGD8ODBAyxcuBCOjo5c4kBLKlurV2hStx0RERFpF1d4JCIiInpLp06dwunTp9GoUSNdh/JOae5hB2dbCZLTslSuKyUC4GQrQXMPO+BFprbDIyIiojdQe6FzIiIiIlKtbt26ePHiha7DeOeIjUQIC/RUua9wrFpYoCfERhy5RkREpI9YlCIiIiJ6S4sXL8bUqVNx5MgRpKamIj09XeFBmtPFyxmRQ7xRycpUYbuTrQSRQ7zRxcu5YIOpKbBxY8HD1FTFmYiIiEjbOH2PiIiI6C116dIFANCxY0eF7YIgQCQSIT8/XxdhvTM6ezrhr9RMLPrtBipamODLwd5oWd1ecYSUiQkQHKyzGImIiEgZi1JEREREb+nw4cO6DuGdFXNNioi98ZCmFdyJ70lmLqb9eAVhgZ4vR0kRERGRXmJRioiIiOgttWvXTtchvJNirkkxdvNFpUXOk9OyMHbzRcXpe3l5wP79Bc8DAgBjdoOJiIh0jd/GRERERG/p2LFjxe5v27atliJ5d+TLBETsjVd51z0BBQudR+yNR2dPp4JpfNnZQI8eBQ0yMliUIiIi0gP8NiYiIiJ6S+3bt1faJhK9XM+Ia0qVvXOJj+VT9lQRAEjTsnAu8TF8a9hrLzAiIiJSG+++R0RERPSWnjx5ovBISUlBTEwMmjVrhgMHDug6vHIp5VnRBanStCMiIiLt40gpIiIiordka2urtK1z584wMzPDlClTEBcXp4OoyrfK1pIybUdERETax5FSRERERBri4OCAmzdv6jqMcqm5hx2cbSUQFbFfBMDZVoLmHnbaDIuIiIhKgCOliIiIiN7SH3/8ofBaEARIpVIsXrwYjRo10lFU5ZvYSISwQE+M3XwRIkBhwfPCQlVYoGfBIudERESkl1iUIiIiInpLjRs3hkgkgiAo3guuZcuW2LBhg46iKv+6eDkjcog3wn+5juT0bPl2J1sJwgI90cXLWYfRERER0ZuwKEVERET0lhITExVeGxkZwcHBARIJ1zPStC5ezmhQpQJaLfkdIhGwZWQLtKhurzxCytQU+PLLl8+JiIhI51iUIiIiInpLbm5uug7hnZUvE3D0zxQAgI3ERHVBCgBMTIBx47QcHRERERWHC50TERERldLvv/8OT09PpKenK+1LS0tD/fr1cfz4cR1E9m6IuSZF6yW/4+Pd1wAAaS9y0XrJ74i5JtVxZERERKQOFqWIiIiISmnlypUYPXo0bGxslPbZ2triww8/xPLly3UQWfkXc02KsZsvQpqWpbA9OS0LYzdfVC5M5ecDR44UPPLztRYnERERFY1FKSIiIqJSunLlCrp06VLkfn9/f8TFxWkxondDvkxAxN54CCr2FW6L2BuPfNkrLbKygPfeK3hkZak4koiIiLSNRSkiIiKiUnr48CFMTEyK3G9sbIx///1XixG9G84lPlYaIfUqAYA0LQvnEh9rLygiIiIqMRaliIiIiEqpSpUquHr1apH7//jjDzg7O2sxondDyjP1Rjqp246IiIh0g0UpIiIiolLq1q0bPvnkE2SpmA724sULhIWFoUePHjqIrHyrbC0p03ZERESkG8a6DoCIiIjIUM2ZMwe7du1C7dq1MX78eNSpUwcikQgJCQn46quvkJ+fj9mzZ+s6zHKnuYcdnG0lSE7LUrmulAiAk60EzT3stB0aERERlQCLUkRERESl5OjoiFOnTmHs2LGYNWsWBKGgRCISiRAQEIA1a9bA0dFRx1GWP2IjEcICPTF280WIAIXClOi//w8L9ITYSKTiaCIiItIXLEoRERERvQU3NzdER0fjyZMnuH37NgRBQK1atVCxYkVdh1audfFyRuQQb0TsjVdY9NzJVoKwQE908eJaXkRERPqORSkiIiKiMlCxYkU0a9ZM12G8U7p4OaNTPUfUnvMbZALw1WBvdPFyUj1CysQE+Oyzl8+JiIhI51iUIiIiIiKD9TwnH7L/5u91rFe56Cl7pqbA9OnaC4yIiIjeiHffIyIiIiKDlZqRDQAwFYtwKekp8mWqlj4nIiIifcSiFBEREREZpJhrUrz/9WkAQE6+gEHrzqD1kt8Rc02q3Dg/Hzh/vuCRn6/lSImIiEgVFqWIiIiIyODEXJNi7OaLSH2eo7A9OS0LYzdfVC5MZWUBzZsXPLKyQERERLrHohQRERERGZR8mYCIvfFQNVGvcFvE3nhO5SMiItJzLEoRERERkUE5l/gY0rSiRzsJAKRpWTiX+Fh7QREREVGJsShFRERERAYl5Zl60+/UbUdERES6waIUERERERmUytaSMm1HREREusGiFBEREREZlOYednC2lUBUxH4RAGdbCZp72GkzLCIiIiohFqWIiIiIyKCIjUQIC/RUua+wUBUW6AmxUVFlKyIiItIHxroOgIiIiIiopLp4OSNyiDfGb72EvFfusudkK0FYoCe6eDkrHmBiAoSFvXxOREREOseiFBEREREZpC5ezrC3vI6Hz7Ixzb8OmrpVRHMPO9UjpExNgfBwrcdIRERERWNRioiIiIgMkiAIePIiFwDQq7ELXO0sdBwRERERlQTXlCIiIiIig5SRnYecPBkA4FZKBvJfmcanRCYDrl8veMhkWoqQiIiIisORUkRERERkcGKuSTH35+vy1yM2nYdzUetJAcCLF4CXV8HzjAzA0lJLkRIREVFROFKKiIiIiAxKzDUpxm6+iH+fZStsT07LwtjNFxFzTaqjyIiIiKgkWJQiIiIi0jNr1qyBh4cHJBIJmjZtiuPHjxfbfsuWLWjUqBEsLCzg7OyMDz74AKmpqfL9mzZtgkgkUnpkZWW91XV1IV8mIGJvPFRN1CvcFrE3vvipfERERKQXWJQiIiIi0iPbt2/H5MmTMXv2bFy6dAlt2rRB165dkZSUpLL9iRMnMGzYMIwcORLXr1/Hjz/+iPPnz2PUqFEK7WxsbCCVShUeEomk1NfVlXOJjyFNyypyvwBAmpaFc4mPtRcUERERlQqLUkRERER6ZPny5Rg5ciRGjRqFevXqYeXKlXB1dUVkZKTK9mfOnIG7uzsmTpwIDw8PtG7dGh9++CEuXLig0E4kEsHJyUnh8TbX1ZWUZ0UXpErTjoiIiHSHRSkiIiIiPZGTk4O4uDj4+/srbPf398epU6dUHuPn54e///4b0dHREAQBDx8+xE8//YTu3bsrtMvIyICbmxuqVq2KHj164NKlS291XV2pbC15c6MStCMiIiLd4d33iIiIiPTEo0ePkJ+fD0dHR4Xtjo6OSE5OVnmMn58ftmzZggEDBiArKwt5eXno2bMnVq9eLW9Tt25dbNq0CQ0aNEB6ejpWrVqFVq1a4cqVK6hVq1aprgsA2dnZyM5+udh4eno6ACA3Nxe5ubklfv9FKTxXbm4umlS1hpONGR6mZ6tcV0oEwMnWDE2qWivGkJsLk1fPV4bxlRev5pk0h3nWPOZYO5hnzTPkHKsbM4tSRERERHpGJBIpvBYEQWlbofj4eEycOBGffPIJAgICIJVKMX36dIwZMwbr168HALRs2RItW7aUH9OqVSt4e3tj9erV+OKLL0p1XQBYtGgRIiIilLYfOHAAFhYWb36jJRQbGwsA6OYkwob0wgH/r8YnQADQ1TET+2N+UzhWlJuLer17AwASDh6EYGICUq0wz6RZzLPmMcfawTxrniHmODMzU612LEoRERER6YlKlSpBLBYrjU5KSUlRGsVUaNGiRWjVqhWmT58OAGjYsCEsLS3Rpk0bzJ8/H87OzkrHGBkZoVmzZrh161aprwsAs2bNQmhoqPx1eno6XF1d4e/vDxsbG/XetBpyc3MRGxuLzp07w8TEBN0AeF9/iKk/XUV2nkzeztlWgtld6yKgfhEx9+oFAHAvs8jKl9fzTJrBPGsec6wdzLPmGXKOC0dPvwmLUkRERER6wtTUFE2bNkVsbCz69Okj3x4bG4te/xVUXpeZmQljY8UunVgsBlAw0kkVQRBw+fJlNGjQoNTXBQAzMzOYmZkpbTcxMdFI5/nV8/ZoXBVbzt3H6buPMczXDV29nNHcww5io6JHdpF6NPX5kSLmWfOYY+1gnjXPEHOsbrwsShERERHpkdDQUAwdOhQ+Pj7w9fXFN998g6SkJIwZMwZAweikBw8e4PvvvwcABAYGYvTo0YiMjJRP35s8eTKaN28OFxcXAEBERARatmyJWrVqIT09HV988QUuX76Mr776Su3r6qP0rDwAwHt1K8O3hn3xjWUyICmp4Hm1aoAR7/dDRESkayxKEREREemRAQMGIDU1FfPmzYNUKoWXlxeio6Ph5uYGAJBKpUgqLK4ACA4OxrNnz/Dll19i6tSpqFChAjp06IAlS5bI2zx9+hT/+9//kJycDFtbWzRp0gTHjh1D8+bN1b6uPkp7UbCIqq25Gr/GvngBeHgUPM/IACwtNRgZERERqYNFKSIiIiI9ExISgpCQEJX7Nm3apLRtwoQJmDBhQpHnW7FiBVasWPFW19U3+TIBjzMK7vx399/naFS1AqfuERERGRiOWyYiIiIigxJzTYpWi39HZm7BIufTfryC1kt+R8w1qY4jIyIiopJgUYqIiIiIDEbMNSnGbr6I5PQshe3JaVkYu/kiC1NEREQGhEUpIiIiIjII+TIBEXvjoeqegoXbIvbGI1+m+q6DREREpF9YlCIiIiIig3DhryeQpmUVuV8AIE3LwrnEx9oLioiIiEqNRSkiIiIiMggpz7LVbFd04YqIiIj0B+++R0REREQGobK1mZrtJMobjY2BwjsLGrMLTEREpA/4jUxEREREBsHHrSKcbSVITstSua6UCICTrQTNPeyUd5qZAV99pekQiYiIqAQ4fY+IiIiIDILYSISwQE+V+0T//X9YoCfERiKVbYiIiEi/sChFRERERAaji5czIod4w9JMrLDdyVaCyCHe6OLlrPpAQQD+/bfgIfDufERERPqA0/eIiIiIyKB08XLGsVv/YuvZ+wio74RgP3c097ArfoRUZiZQuXLB84wMwNJSO8ESERFRkViUIiIiIiKD8ywrHwDQ3MMOvjXsdRwNERERlQan7xERERGRwUl7kQsAsDU30XEkREREVFosShERERGRwUn/ryhlI+HAfyIiIkPFohQRERERGZR8mYCHaVkAgL+fZCJfxoXLiYiIDBGLUkRERERkMGKuSdF6ye+QphcUpeb9moDWS35HzDWpjiMjIiKiktJ5UWrNmjXw8PCARCJB06ZNcfz48SLb7tq1C507d4aDgwNsbGzg6+uL/fv3azFaIiIiItKV/dcfYuzmi5D+N0qqUHJaFsZuvsjCFBERkYHRaVFq+/btmDx5MmbPno1Lly6hTZs26Nq1K5KSklS2P3bsGDp37ozo6GjExcXhvffeQ2BgIC5duqTlyImIiIhIm2QCMD/6BlRN1CvcFrE3vuipfMbGwPDhBQ9jrkNFRESkD3T6jbx8+XKMHDkSo0aNAgCsXLkS+/fvR2RkJBYtWqTUfuXKlQqvFy5ciJ9//hl79+5FkyZNtBEyEREREenAnXQRktOzi9wvAJCmZeFc4mP41rBXbmBmBmzapLH4iIiIqOR0VpTKyclBXFwcZs6cqbDd398fp06dUuscMpkMz549g52dXZFtsrOzkZ39sgOTnp4OAMjNzUVubm4pIi9a4flEQn6Zn5teKswtc6w5zLF2MM+axxxrhyHn2RBjflelq/lRpTzLenMjIiIi0gs6K0o9evQI+fn5cHR0VNju6OiI5ORktc6xbNkyPH/+HP379y+yzaJFixAREaG0/cCBA7CwsChZ0Gpyz7qD6Og7Gjk3vRQbG6vrEMo95lg7mGfNY461wxDznJmZqesQSE02Juq1q2wtUb1DEIDCz9vCAhCJyiYwIiIiKjWdT6gXvdYhEARBaZsq27ZtQ3h4OH7++WdUrly5yHazZs1CaGio/HV6ejpcXV3h7+8PGxub0geuQm5uLmJjY3FPUgMhHeqU6bnppcI8d+7cGSYmavZQqUSYY+1gnjWPOdYOQ85z4Qhq0n81bAQ42ZjhYXq2ynWlRACcbCVo7lHECPrMTMDKquB5RgZgaampUImIiEhNOitKVapUCWKxWGlUVEpKitLoqddt374dI0eOxI8//ohOnToV29bMzAxmZmZK201MTDTWcRZEYoPrlBsiTX6GVIA51g7mWfOYY+0wxDwbWrzvMiMRMKdbXUyIuqK0r/DnzLBAT4iNOAKKiIjIUOjs7numpqZo2rSp0lD/2NhY+Pn5FXnctm3bEBwcjK1bt6J79+6aDpOIiIiI9ERAfUdEDvGGjUTxd1UnWwkih3iji5ezjiIjIiKi0tDp9L3Q0FAMHToUPj4+8PX1xTfffIOkpCSMGTMGQMHUuwcPHuD7778HUFCQGjZsGFatWoWWLVvKR1mZm5vD1tZWZ++DiIiIiLSji5czrv+TjtW/30arGvYY36EWmnvYcYQUERGRAdJpUWrAgAFITU3FvHnzIJVK4eXlhejoaLi5uQEApFIpkpKS5O3Xrl2LvLw8jBs3DuPGjZNvHz58ODbxFr9ERERE74RnWXkAgEauFeBbw17H0RAREVFp6Xyh85CQEISEhKjc93qh6ciRI5oPiIiIiIj0WnpWLgDAxpxrghERERkyna0pRURERERUGukvCopStixKERERGTSdj5QiIiIiIlJXvkzA/ceZAICH6VnIlwnqrSclFgP9+r18TkRERDrHohQRERERGYT91x9iwW83IU3LAgCsPHgL28/fR1ig55vvvCeRAD/+qIUoiYiISF2cvkdEREREeu9KqggToq7IC1KFktOyMHbzRcRck+ooMiIiIiotFqWIiIiISK/lywTsumcEQcW+wm0Re+ORL1PVgoiIiPQVi1JEREREpNcu/PUET3OKXjdKACBNy8K5xMdFn+T5c0AkKng8f172QRIREVGJsShFRERERHot5Vm2mu2y3tyIiIiI9AaLUkRERESk1ypbm6nZTqLhSIiIiKgssShFRERERHrNx60iKpgKKGoCnwiAs60EzT3stBkWERERvSUWpYiIiIhIr4mNROjrLlO5r7BQFRboCbFR0etOERERkf5hUYqIiIiI9F4jewGrBzZCBXMThe1OthJEDvFGFy9nHUVGREREpWWs6wCIiIiIiNQRUN8RqS/yEPbzdTSsaotZXeuhuYcdR0gREREZKBaliIiIiMhgZGTlAQDqOlnDt4a9+geKxUC3bi+fExERkc6xKEVEREREBiPtRS4AwPa1aXxvJJEA+/ZpICIiIiIqLa4pRUREREQGI720RSkiIiLSOyxKEREREZFByJcJuPtvBgAg9XkO8mWCjiMiIiKit8GiFBERERHpvSupIrRfdgzn7j0BAGw8eQ+tl/yOmGtS9U7w/DlgaVnweP5cg5ESERGRuliUIiIiIiK9tv/6Q2z40wjJ6dkK25PTsjB280X1C1OZmQUPIiIi0gssShERERGR3sqXCZgffUPlvsLJexF74zmVj4iIyACxKEVEREREeutc4uP/RkiJVO4XAEjTsnAu8bFW4yIiIqK3x6IUEREREemtlGdZZdqOiIiI9AeLUkRERESktypbS8q0HREREekPFqWIiIiISG8197CDk40ZXq4gpUgEwNlWguYedlqNi4iIiN4ei1JEREREpLfERiLM6VZX5b7CVabCAj0hNlK95pSckRHQrl3Bw4hdYCIiIn3Ab2QiIiIiPbNmzRp4eHhAIpGgadOmOH78eLHtt2zZgkaNGsHCwgLOzs744IMPkJqaKt+/bt06tGnTBhUrVkTFihXRqVMnnDt3TuEc4eHhEIlECg8nJyeNvL+SCqjviBG1ZbC3NFXY7mQrQeQQb3Txcn7zSczNgSNHCh7m5hqJk4iIiEqGRSkiIiIiPbJ9+3ZMnjwZs2fPxqVLl9CmTRt07doVSUlJKtufOHECw4YNw8iRI3H9+nX8+OOPOH/+PEaNGiVvc+TIEQwaNAiHDx/G6dOnUa1aNfj7++PBgwcK56pfvz6kUqn8cfXqVY2+15JoZC9gxfsNAACVrc2wbXRLnPiog3oFKSIiItJLLEoRERER6ZHly5dj5MiRGDVqFOrVq4eVK1fC1dUVkZGRKtufOXMG7u7umDhxIjw8PNC6dWt8+OGHuHDhgrzNli1bEBISgsaNG6Nu3bpYt24dZDIZDh06pHAuY2NjODk5yR8ODg4afa8llZmbD6BgDSnfGvZvnrJHREREes1Y1wEQERERUYGcnBzExcVh5syZCtv9/f1x6tQplcf4+flh9uzZiI6ORteuXZGSkoKffvoJ3bt3L/I6mZmZyM3NhZ2d4uLgt27dgouLC8zMzNCiRQssXLgQ1atXL/I82dnZyM7Olr9OT08HAOTm5iI3N/eN71ddhedKz8wBAFiYikt+/ufPYVyrFgAg79YtwNKyzOIrLwpzWpafHSljnjWPOdYO5lnzDDnH6sbMohQRERGRnnj06BHy8/Ph6OiosN3R0RHJyckqj/Hz88OWLVswYMAAZGVlIS8vDz179sTq1auLvM7MmTNRpUoVdOrUSb6tRYsW+P7771G7dm08fPgQ8+fPh5+fH65fvw57e3uV51m0aBEiIiKUth84cAAWFhbqvOUSuXDlKgAxMp48QnR0dImOFWdlocejRwCA/fv3I18iKfP4yovY2Fhdh/BOYJ41jznWDuZZ8wwxx5mZmWq1Y1GKiIiISM+IRIrT0gRBUNpWKD4+HhMnTsQnn3yCgIAASKVSTJ8+HWPGjMH69euV2n/22WfYtm0bjhw5AskrhZmuXbvKnzdo0AC+vr6oUaMGvvvuO4SGhqq89qxZsxT2paenw9XVFf7+/rCxsSnRey5Obm4uYmNjUa16beDuHdSoVgXdujUo2UmeP5c/DQgI4EgpFQrz3LlzZ5iYmOg6nHKLedY85lg7mGfNM+QcF46efhMWpYiIiIj0RKVKlSAWi5VGRaWkpCiNniq0aNEitGrVCtOnTwcANGzYEJaWlmjTpg3mz58PZ+eXC4EvXboUCxcuxMGDB9GwYcNiY7G0tESDBg1w69atItuYmZnBzMxMabuJiYlGOs9ZeQIAwNrctOTnf6W9iYmJwmtSpKnPjxQxz5rHHGsH86x5hphjdePlQudEREREesLU1BRNmzZVGqYfGxsLPz8/lcdkZmbCyEixSycWiwEUjLAq9Pnnn+PTTz9FTEwMfHx83hhLdnY2EhISFIpauvY8p2Chc0sz/q5KRERUHrAoRURERKRHQkND8e2332LDhg1ISEjAlClTkJSUhDFjxgAomDI3bNgwefvAwEDs2rULkZGRuHv3Lk6ePImJEyeiefPmcHFxAVAwZW/OnDnYsGED3N3dkZycjOTkZGRkZMjPM23aNBw9ehSJiYk4e/Ys+vXrh/T0dAwfPly7CSiCTAASHxVMwXv8PBv5MuENRxAREZG+489MRERERHpkwIABSE1Nxbx58yCVSuHl5YXo6Gi4ubkBAKRSKZKSkuTtg4OD8ezZM3z55ZeYOnUqKlSogA4dOmDJkiXyNmvWrEFOTg769euncK2wsDCEh4cDAP7++28MGjQIjx49goODA1q2bIkzZ87Ir6tL+68/RMRFMZ7mFCxUvuPC3zh+6xHCAj3RxUt/RnIRERFRybAoRURERKRnQkJCEBISonLfpk2blLZNmDABEyZMKPJ89+7de+M1o6Ki1A1Pq2KuSTEh6gpeHxeVnJaFsZsvInKIt3qFKSMjoHDaohEnCxAREekDfiMTERERkV7KlwmI2Bv/X0HqtTsS/vf/EXvj1ZvKZ24OnD9f8DA3L+NIiYiIqDQ4UoqIygWZTIacnBxdh1Eiubm5MDY2RlZWFvLz83UdTrnEHGuHPufZxMREvug3GZ5ziY8hTcsqcr8AQJqWhXOJj+Fbw157gREREVGZYFGKiAxeTk4OEhMTIZPJdB1KiQiCACcnJ9y/fx8ikejNB1CJMcfaoe95rlChApycnPQyNipeyrOiC1KlaUdERET6hUUpIjJogiBAKpVCLBbD1dVV6bbo+kwmkyEjIwNWVlYGFbchYY61Q1/zLAgCMjMzkZKSAgBwduaC2IamsrWk7NplZgKengXP4+MBC4u3iIyIiIjKAotSRGTQ8vLykJmZCRcXF1gY2H9gFE45lEgkevUf8uUJc6wd+pxn8//WDkpJSUHlypU5lc/ANPewg7OtBMlpWUoLnQMFq0w52UrQ3MPuzScTBOCvv14+JyIiIp3Tr54jEVEJFa5fY2pqquNIiEhfFRasc3NzdRwJlZTYSISwwP9GN71WliqcjBkW6AmxEadmEhERGSIWpYioXOBaMURUFP77wbB18XLG6oGNUOG13x6cbCWIHOKNLl6clklERGSoWJQiIiKVRCIR9uzZozfn0bXg4GD07t1b7fb37t2DSCTC5cuXNRZTIV3lWJvvkd5tAfUdMafJyzs7fjvMByc+6sCCFBERkYFjUYqISEeSk5MxY8YM1KxZE2ZmZnB1dUVgYCAOHTqk69BKJTw8HI0bN1baLpVK0bVrV+0HpGOurq6QSqXw8vLSdSga8y68R9Ifsldm77WqWYlT9oiIiMoBLnRORKQD9+7dQ6tWrWBjY4PFixejcePGyM3Nxf79+zFu3DjcuHFD1yGWGScnJ12HoBNisbhcv/ecnByYmprCyckJMplM1+HQOyDvlT8zU2P+rkpERFQe8BudiEgHQkJCIBKJcPDgQfTr1w+1a9dG/fr1ERoaijNnzgBQPTXq6dOnEIlEOHLkCADgyJEjEIlE2L9/P5o0aQJzc3N06NABKSkp+O2331CvXj3Y2Nhg0KBByMzMlJ/H3d0dK1euVIipcePGCA8PLzLmjz76CLVr14aFhQWqV6+OuXPnyheO3rRpEyIiInDlyhWIRCKIRCJs2rQJgOLUMl9fX8ycOVPhvP/++y9MTExw+PBhAAXFjhkzZqBKlSqwtLREixYt5O+3KGlpafjf//6HypUrw8bGBh06dMCVK1cAAI8ePYKLiwsWLlwob3/27FmYmpriwIEDAF6O8lq7di1cXV1hYWGB999/H0+fPi3ymjExMWjdujUqVKgAe3t79OjRA3fu3JHvf/3zK/ysDh06BB8fH1hYWMDPzw83b95UOO/evXvRtGlTSCQSVK9eHREREcjLy5Pvv3XrFtq2bQuJRAJPT0/ExsYWm5u1a9eiSpUqSoWjnj17Yvjw4QCAO3fuoFevXnB0dISVlRWaNWuGgwcPKrR3d3fH/PnzERwcDFtbW4wePVrpPebn52PkyJHw8PCAubk56tSpg1WrVimcp3Aa5NKlS+Hs7Ax7e3uMGzdOYRHy7OxszJgxA66urjAzM0OtWrWwfv16+f74+Hh069YNVlZWcHR0xNChQ/Ho0aNi80CGL/e/P2ETsah0o6REIsDTs+DBdcaIiIj0AotSRFSuCIKAzJw8nTwENW8x/vjxY8TExCAkJASWlpZK+ytUqFDi9x0eHo4vv/wSp06dwv3799G/f3+sXLkSW7duxb59+xAbG4vVq1eX+Lyvsra2xqZNmxAfH49Vq1Zh3bp1WLFiBQBgwIABmDp1KurXrw+pVAqpVIoBAwYonSMoKAjbtm1TyNX27dvh6OiIdu3aAQA++OADnDx5ElFRUfjjjz/w/vvvo0uXLrh165bKuARBQPfu3ZGcnIzo6GjExcXB29sbHTt2xOPHj1GpUiV8++23CA8Px4ULF5CRkYEhQ4YgJCQE/v7+8vPcvn0bO3bswN69exETE4PLly9j3LhxRebj+fPnCA0Nxfnz53Ho0CEYGRmhT58+bxw1NHv2bCxbtgwXLlyAsbExRowYId+3f/9+DBkyBBMnTkR8fDzWrl2LTZs2YcGCBQAAmUyGvn37QiwW48yZM/j666/x0UcfFXu9999/H48ePZIX/QDgyZMn2L9/P4KCggAAGRkZ6NatGw4ePIhLly4hICAAgYGBSEpKUjjX559/Di8vL8TFxWHu3LlK15LJZKhatSp27NiB+Ph4fPLJJ/j444+xY8cOhXaHDx/GnTt3cPjwYXz33XfYtGmTvIgJAMOGDUNUVBS++OILJCQk4Ouvv4aVlRWAgumg7dq1Q+PGjXHhwgXExMTg4cOH6N+/f7F5IMOX99+/NsyMxaU7gYUFcP16weO/OzISERGRbnH6HhGVKy9y8+H5yX6dXDt+XgAsTN/8r9Xbt29DEATUqVOnzK49f/58tGrVCgAwcuRIzJo1C3fu3EH16tUBAP369cPhw4ffWMAozpw5c+TP3d3dMXXqVGzfvh0zZsyAubk5rKysYGxsXOyUtQEDBmDKlCk4ceIE2rRpAwDYunUrBg8eDCMjI9y5cwfbtm3D33//DRcXFwDAtGnTEBMTg40bNyqMdip0+PBhXL16FSkpKTAzMwMALF26FHv27MFPP/2EgQMHolu3bhg9ejSCgoLQrFkzSCQSLF68WOE8WVlZ+O6771C1alUAwOrVq9G9e3csW7ZM5Xv6v//7P4XX69evR+XKlREfH1/sGksLFiyQF+BmzpyJ7t27IysrCxKJBAsWLMDMmTPlI5iqV6+OTz/9FDNmzEBYWBgOHjyIhIQE3Lt3Tx7nwoULi12zy87ODl26dMHWrVvRsWNHAMCPP/4IOzs7+etGjRqhUaNG8mPmz5+P3bt345dffsH48ePl2zt06IBp06bJX9+7d0/hWiYmJoiIiJC/9vDwwKlTp7Bjxw6FolHFihXx5ZdfQiwWo27duujevTsOHTqE0aNH488//8SOHTsQGxuLTp06yfNQKDIyEt7e3gp/Cxs2bICrqyv+/PNP1K5du8hckGErHCllxql7RERE5Qa/1YmItKxwlFBZ3qa+YcOG8ueOjo7yKXavbktJSXmra/z0009o3bo1nJycYGVlhblz5yqNpHkTBwcHdO7cGVu2bAEAJCYm4vTp0/IROxcvXoQgCKhduzasrKzkj6NHjypMjXtVXFwcMjIyYG9vr3BMYmIi7t69K2+3dOlS5OXlYceOHdiyZQskEonCeapVqyYv9AAFUw1lMpnS9LpCd+7cweDBg1G9enXY2NjAw8MDAN6Yk1c/K2fngjuHFX42cXFxmDdvnsL7GD16NKRSKTIzM5GQkKAyzjcJCgrCzp07kZ2dDQDYsmULBg4cCLG4YMTJ8+fPMWPGDHh6eqJChQqwsrLCjRs3lN6Lj4/PG6/19ddfw8fHBw4ODrCyssK6deuUzlO/fn35tQvzUJiDy5cvQywWywt3r4uLi8Phw4cVclS3bl0AKPJvhMqHwjWluJ4UERFR+cGRUkRUrpibiBE/L0Bn11ZHrVq1IBKJcOPGDXTo0KHIdkZGBf/h9epUt1fX3XmViYmJ/LlIJFJ4Xbjt1WllRkZGStMNizo3AJw5cwYDBw5EREQEAgICYGtri6ioKCxbtqzIY4oSFBSESZMmYfXq1di6dSvq168vH6Ujk8kgFosRFxenULQAIJ++9TqZTAZnZ2eV607Z2NjIn9+9exf//PMPZDIZ/vrrL4XikCqFRcOiioeBgYFwdXXFunXr4OLiAplMBi8vL+Tk5BR73tc/q8L3UPj/ERER6Nu3r9JxEolE5RRRdYqbgYGBkMlk2LdvH5o1a4bjx49j+fLl8v3Tp0/H/v37sXTpUtSsWRPm5ubo16+f0ntRNd30VTt27MCUKVOwbNky+Pr6wtraGp9//jnOnj2r0K64v09zc/NiryGTyRAYGIglS5Yo7Sss8lH59HL6XimLUpmZQLNmBc/Pn+cUPiIiIj3AohQRlSsikUitKXS6ZGdnh4CAAKxZswbDhw9XKJwABYuZV6hQAQ4ODgAK1tBp0qQJACgsev42HBwcIJVK5a/T09ORmJhYZPuTJ0/Czc0Ns2fPlm/766+/FNqYmpoiPz//jdfu3bs3PvzwQ8TExGDr1q0YOnSofF+TJk2Qn5+PlJQU+fS+N/H29kZycjKMjY3h7u6usE8mkyE9PR05OTkICgrCgAEDULduXYwcORJXr16Fo6OjvG1SUhL++ecf+bTB06dPw8jISOV0sNTUVCQkJGDt2rXyOE+cOKFWvG96Lzdv3kTNmjVV7vf09FQZ55uYm5ujb9++2LJlC27fvo3atWujadOm8v3Hjx9HcHAw+vTpA6BgjanXp+ap48SJE/Dz80NISIh8W0lHLzVo0AAymQxHjx6VT997lbe3N3bu3Al3d3cYG+v3P+tUtnJlBQXYUq8pJQhAfPzL50RERKRzHP9MRKQDa9asQX5+Pjp16oSdO3fi1q1bSEhIwBdffCGfjmVubo6WLVti8eLFiI+Px7FjxxTWdXobHTp0wA8//IDjx4/j2rVrGD58uNLIpFfVrFkTSUlJiIqKwp07d/DFF19g9+7dCm3c3d2RmJiIy5cv49GjR/KpYq+ztLREr169MHfuXCQkJGDw4MHyfbVr10ZQUBCGDRuGXbt2ITExEefPn8eSJUsQHR2t8nydOnWCr68vevfujf379+PevXs4deoU5syZgwsXLgAoWA8rLS0NX3zxBWbMmIF69eph5MiRCueRSCQYPnw4rly5guPHj2PixIno37+/yvWkKlasCHt7e3zzzTe4ffs2fv/9d4SGhhaZP3V98skn+P777xEeHo7r168jISEB27dvl3/unTp1Qp06dTBs2DB5nK8WCosTFBSEffv2YcOGDRgyZIjCvpo1a2LXrl24fPkyrly5gsGDB79xwXZVatasiQsXLmD//v34888/MXfuXJw/f75E53B3d8fw4cMxYsQI7NmzB4mJiThy5Ih8sfRx48bh8ePHGDRoEM6dO4e7d+/iwIEDGDFihFpFUTJc8jWlTNh9JSIiKi/4rU5EpAMeHh64cOECWrdujenTp8PLywudO3fGoUOHEBkZKW+3YcMG5ObmwsfHB5MmTcL8+fPL5PqzZs1C27Zt0aNHD3Tr1g29e/dGjRo1imzfq1cvTJkyBePHj0fjxo1x6tQppbuv/d///R+6dOmC9957Dw4ODti2bVuR5wsKCsKVK1fQpk0bVKtWTWHfxo0bMWzYMEydOhV16tRBz549cfbsWbi6uqo8l0gkQnR0NNq2bYsRI0agdu3aGDhwIO7duwdHR0ecOHECq1atwg8//AAbGxsYGRnhhx9+wIkTJxRyXbNmTfTt2xfdunWDv78/vLy8sGbNGpXXNDIyQlRUFOLi4uDl5YUpU6bg888/L/L9qisgIAC//vorYmNj0axZM7Rs2RLLly+Hm5ub/Lq7d+9GdnY2mjdvjlGjRsnvzPcmHTp0gJ2dHW7evKlQCASAFStWoGLFivDz80NgYCACAgLg7e1d4vg//PBD9O3bFwMGDECLFi2QmpqqMGpKXZGRkejXrx9CQkJQt25djB49Gs+fPwcAuLi44OTJk8jPz0dAQAC8vLwwadIk2Nrayqe8UvmUx4XOiYiIyh2RoO49zMuJ9PR02NraIi0tTWnKzNvKzc1FdHQ0Es1rY7J/vTI9N71UmOdu3boprUtCZcOQcpyVlYXExER4eHgoLVyt7wqnlhUWSqjsqZvj8PBw7Nmzp8ymR75r9P1vubh/T2iyX/Au0lQ+c3NzMe+73/DDbTFa16yEzaNalPwkz58DhWvTZWQAb1gj7V1kSN//hox51jzmWDuYZ80z5Byr2yfQv54jEREREdFrChc65933iIiIyg9+qxMRERGR3svl9D0iIqJyh9/qRET0zgsPD+fUPSI999ZrSolEgJtbwUMkKrvAiIiIqNR4L2UiIiIi0nuF0/fMjIu+U2ixLCyAe/fKLB4iIjI8giAgLy/PYO7Ym5ubC2NjY2RlZeldzGKxGMbGxhC95Q89LEoRERERkd7LlRV0es1MONCfiIhKLicnB1KpFJmZmboORW2CIMDJyQn3799/6+KPJlhYWMDZ2RmmpqalPgeLUkRERESk9956+h4REb2zZDIZEhMTIRaL4eLiAlNTU70s8rxOJpMhIyMDVlZWenWHY0EQkJOTg3///ReJiYmoVatWqeNjUYqIiIiI9F7hQuelvvveixdA27YFz48dA8zNyyYwIiLSezk5OZDJZHB1dYWFhYWuw1GbTCZDTk4OJBKJXhWlAMDc3BwmJib466+/5DGWBotSRERERKT3Xo6UKuWaUjIZcOHCy+dERPTO0bfCjqEri3zyEyEiIiIivZYvE5CaXfA8Oe0F8mWCbgMiIiKiMsGiFBGRAROJRNizZw8A4N69exCJRLh8+XKpjlelNOdUh7u7O1auXFmm5yypI0eOQCQS4enTp2of0759e0yePFljMRUKDg5G7969NX4dVbT1HonUFXNNivbLjuFmWkG3deu5+2i95HfEXJPqODIiIiJ6W5y+R0Tl0orYP7V6vSmda5eofXBwMJ48eYLvvvuuzGJwdXWFVCpFpUqV1D5GKpWiYsWKZRZDebdr1y6YmJjoOgyNehfeIxmOmGtSjN18Ea+Pi0pOy8LYzRcROcQbXbycdRIbERGRNoSEhGDbtm0AAGNjY7i6uqJv376IiIiApaUlAGDnzp1YvXo1Ll26hPz8fFSvXh39+vXD+PHjYWdnh127diEyMhKXL19GdnY26tevj/DwcAQEBChca+fOnZg7dy7u3LmDGjVqYMGCBejTp49G3x9HShERlRNisRhOTk4wNlb/9wYnJyeYmZlpMKryxc7ODtbW1roOQyNyc3MBlO/3SIYlXyYgYm+8UkEKgHxbxN54TuUjIqJyLyAgAFKpFHfv3sX8+fOxZs0aTJs2DQAwe/ZsDBgwAM2aNcNvv/2Ga9euYdmyZbhy5Qp++OEHAMCxY8fQuXNnREdHIy4uDu+99x4CAwNx6dIl+TVOnz6NAQMGYOjQobhy5QqGDh2K/v374+zZsxp9byxKERHpgfbt22PixImYMWMG7Ozs4OTkhPDwcIU2t27dQtu2bSGRSODp6YnY2FiF/a9OtZPJZKhatSq+/vprhTYXL16ESCTC3bt3AShP3zt37hyaNGkCiUQCHx8fhS8qANi0aRMqVKigsG3Pnj0Kt9S9c+cOevXqBUdHR1hZWaFZs2Y4ePBgiXOyceNG1KtXDxKJBHXr1sWaNWvk+0aMGIGGDRsiO7tgkZnc3Fw0bdoUQUFBCrmIioqCv78/LCwsUL9+fRw5cqTI66WmpmLQoEGoWrUqLCws0KBBA/mvUoVen9rm7u6OhQsXYsSIEbC2tka1atXwzTffKBzz4MEDDBgwABUrVoS9vT169eqFe/fuyffn5+cjNDQUFSpUgL29PWbMmAFBKPo/stPS0mBubo6YmBiF7bt27YKlpSUyMjIAAB999BFq164NCwsLVK9eHXPnzpUXngAgPDwcjRs3xoYNG1C9enWYmZlBEASl97h582b4+PjA2toaTk5OGDx4MFJSUuT7jxw5ArFYjKNHj6J58+awsLCAn58fbt68qRDfL7/8Ah8fH0gkElSqVAl9+/aV78vJycGMGTNQpUoVWFpaokWLFsV+VvRuOJf4GNK0rCL3CwCkaVk4l/hYe0ERERHpgJmZGZycnODq6orBgwcjKCgIe/bswblz57Bw4UIsW7YMn3/+Ofz8/ODu7o7OnTtj586dGD58OABg5cqVmDFjBpo1a4ZatWph4cKFqFWrFvbu3Su/xsqVK9G5c2fMmjULdevWxaxZs9CxY0eNL7nBohQRkZ747rvvYGlpibNnz+Kzzz7DvHnz5IUnmUyGvn37QiwW48yZM/j666/x0UcfFXkuIyMjDBw4EFu2bFHYvnXrVvj6+qJ69epKxzx//hw9evRAnTp1EBcXh/DwcPkvMCWRkZGBbt264eDBg7h06RICAgIQGBiIpKQktc+xbt06zJ49GwsWLEBCQgIWLlyIuXPnyqc7fvHFF3j+/DlmzpwJAJg7dy4ePXqkULgCCgoz48ePR1xcHPz8/NCzZ0+kpqaqvGZWVhaaNm2KX3/9FdeuXcP//vc/DB069I2/Di1btkxewAsJCcHYsWNx48YNAEBmZibee+89WFlZ4dixYzhx4gSsrKzQpUsX5OTkyI/fsGED1q9fjxMnTuDx48fYvXt3kdeztbVF9+7dVX62vXr1gpWVFQDA2toamzZtQnx8PFatWoV169ZhxYoVCsfcvn0bO3bswM6dO4tcNywnJweffvoprly5gj179iAxMRHBwcFK7ebPn4/PP/8cFy5cgLGxMUaMGCHft2/fPvTt2xfdu3fHpUuXcOjQIfj4+Mj3f/DBBzh58iSioqLwxx9/4P3330eXLl1w69atohNP5V7Ks6ILUqVpBwCoVKngQUREVOj586IfWVnqt33x4s1ty4i5uTlyc3OxZcsWWFlZISQkRGW7139MLiSTyfDs2TPY2dnJt50+fRr+/v4K7QICAnDq1Kkyi1sVrilFRKQnGjZsiLCwMABArVq18OWXX+LQoUPo3LkzDh48iISEBNy7dw9Vq1YFACxcuBBdu3Yt8nxBQUFYvnw5/vrrL7i5uUEmkyEqKgoff/yxyvZbtmxBfn4+NmzYIB9Z9Pfff2Ps2LEleh+NGjVCo0aN5K/nz5+P3bt345dffsH48ePVOsenn36KZcuWyUfTeHh4ID4+HmvXrsXw4cNhZWWFzZs3o127drC2tsayZctw6NAh2NraKpxn3Lhx6NmzJ2xsbBAZGYmYmBisX78eM2bMULpmlSpVFIpwEyZMQExMDH788Ue0aNGiyFi7desm7wh89NFHWLFiBY4cOYK6desiKioKRkZG+Pbbb+WjyTZu3IgKFSrgyJEj8Pf3x8qVKzFr1iz83//9HwDg66+/xv79+4vNT1BQEIYNG4bMzExYWFggPT0d+/btw86dO+Vt5syZI3/u7u6OqVOnYvv27QrvPScnBz/88AMcHByKvNarxaXq1avjiy++QPPmzZGRkSEvgBVer127djAyMsLMmTPRvXt3ZGVlQSKRYMGCBRg4cCAiIiLk7Qv/Ru7cuYNt27bh77//houLCwBg2rRpiImJwcaNG7Fw4cJic0HlV2VrSZm2g6Ul8O+/bxERERGVS6/0Z5R06wbs2/fydeXKQGam6rbt2gGvjvR2dwcePVJsU8xoeHWdO3cOW7duRceOHXHr1i1Ur169xOuBLlu2DM+fP0f//v3l25KTk+Ho6KjQztHREcnJyW8dc3E4UoqISE80bNhQ4bWzs7N8mlRCQgKqVasmL0gBgK+vb7Hna9KkCerWrSufgnb06FGkpKQofPm8KiEhAY0aNYKFhYXa11Dl+fPnmDFjBjw9PVGhQgVYWVnhxo0bao+U+vfff3H//n2MHDkSVlZW8sf8+fNx584dhdimTZuGTz/9FFOnTkXbtm2VztWyZUv5c2NjY/j4+CAhIUHldfPz87FgwQI0bNgQ9vb2sLKywoEDB94Y96ufm0gkgpOTk/xzi4uLw+3bt2FtbS1/H3Z2dsjKysKdO3eQlpYGqVSqkOfCOIvTvXt3GBsb45dffgFQsCiltbW1wq9bP/30E1q3bg0nJydYWVlh7ty5Su/Fzc2t2IIUAFy6dAm9evWCm5sbrK2t0b59ewBQOlf9+vXlz52dCxaeLszD5cuX0bFjR5Xnv3jxIgRBQO3atRU+76NHjyp83vTuae5hB2dbCURF7BcBcLaVoLmHXREtiIiIyod9+/bBysoKEokEvr6+aNu2LVavXg1BEBSW0VDHtm3bEB4eju3bt6Ny5coK+14/V2nOX1IcKUVEpCde/4VDJBJBJpMBgMo1htT5gggKCsLWrVsxc+ZMbN26FQEBAUXena+4dYwKGRkZKbV7dZ0iAJg+fTr279+PpUuXombNmjA3N0e/fv3k09XepPA9r1u3TmmEklgsVmh38uRJiMXiEk3zKipvy5Ytw4oVK7By5Uo0aNAAlpaWmDx58hvjLu5zk8lkaNq0qdJUOwBvLAYVx9TUFP369cPWrVsxcOBAbN26FQMGDJAvcn/mzBn5yKSAgADY2toiKioKy5YtUzhP4R1bivL8+XP4+/vD398fmzdvhoODA5KSkhAQEKCUl1fzUJjjwjyYm5sXeQ2ZTAaxWIy4uDiFzxeAwkgseveIjUQIC/TE2M0XIQIUFjwv/Kc4LNATYiPNdpaJiKic+289TpVe65vglXU1lRi9NubnlTVE31b79u3x9ddfw8TEBC4uLvJ+V+3atXHixAnk5uaqNVpq+/btGDlyJH788Ud06tRJYZ+Tk5PSqKiUlBSl0VNljSOliIgMgKenJ5KSkvDPP//It50+ffqNxw0ePBhXr15FXFwcfvrpJ/lC4EVd48qVK3jxynz4M2fOKLRxcHDAs2fP8PyVOfGvr0V0/PhxBAcHo0+fPmjQoAGcnJwUFvZ+E0dHR1SpUgV3795FzZo1FR4eHh7ydp9//jkSEhJw9OhR7N+/Hxs3blQ616vrQeXl5SEuLg5169ZVed3jx4+jV69eGDJkCBo1aoTq1au/9ZpG3t7euHXrFipXrqz0XmxtbWFrawtnZ2eFPBfG+SZBQUGIiYnB9evXcfjwYYXP9uTJk3Bzc8Ps2bPh4+ODWrVq4a+//ipx/Ddu3MCjR4+wePFitGnTBnXr1lVY5FxdDRs2xKFDh1Tua9KkCfLz85GSkqKUIycnpxJfi8qXLl7OiBziDUcbxbuEOtlKEDnEG128nNU/2YsXQPv2BY/X1/0gIqJ3l6Vl0Q+JRP22r/8Ip6pNqUO0RM2aNeHm5qZQfBo8eDAyMjKU1lUt9PTpU/nzbdu2ITg4GFu3bkX37t2V2vr6+irdSOnAgQPw8/Mrddzq4EgpIiID0KlTJ9SpUwfDhg3DsmXLkJ6ejtmzZ7/xOA8PD/j5+WHkyJHIy8tDr169imw7ePBgzJ49GyNHjsScOXNw7949LF26VKFNixYtYGFhgY8//hgTJkzAuXPnsGnTJoU2NWvWxK5duxAYGAiRSIS5c+fKR8yoKzw8HBMnToSNjQ26du2K7OxsXLhwAU+ePEFoaCguX76MTz75BD/99BNatWqFVatWYdKkSWjXrp3CIu5r1qxBlSpV4O3tjVWrVuHJkycKayS9HvfOnTtx6tQpVKxYEcuXL0dycjLq1atXothfFRQUhM8//xy9evXCvHnzULVqVSQlJWHXrl2YPn06qlatikmTJmHx4sWoVasW6tWrh+XLlyt0IIrSrl07ODo6IigoCO7u7gpTFWvWrImkpCRERUWhWbNm2LdvX7GLpxelWrVqMDU1xerVqzFmzBhcu3YNn376aYnPExYWho4dO6JGjRoYOHAg8vLy8Ntvv2HGjBmoXbu2fI2sZcuWoUmTJnj06BF+//13NGjQAN26dSvx9ah86eLljPa17PHl9hhUr98YzhUs0dzDruQjpGQy4OjRl8+JiIgMXIsWLTBjxgxMnToVDx48QJ8+feDi4oLbt2/j66+/RuvWrTFp0iRs27YNw4YNw6pVq9CyZUv5iChzc3P5mqyTJk1C27ZtsWTJEvTq1Qs///wzDh48iBMnTmj0PXCkFBGRATAyMsLu3buRnZ2N5s2bY9SoUViwYIFaxwYFBeHKlSvo27dvsdOorKyssHfvXsTHx6NJkyaYPXs2lixZotDGzs4OmzdvRnR0NBo0aCCfk/6qFStWoGLFivDz80NgYCACAgLg7e1dovc7atQofPvtt9i0aRMaNGiAdu3aYdOmTfDw8EBWVhaCgoIQHByMwMBAAMDIkSPRqVMnDB06FPn5+fLzLFy4EKtWrUKTJk1w/Phx/Pzzz0VOX5w7dy68vb0REBCA9u3bw8nJCb179y5R3K+zsLDAsWPHUK1aNfTt2xf16tXDiBEj8OLFC9jY2AAApk6dimHDhiE4OBi+vr6wtrZGnz593nhukUiEQYMG4cqVK0oj4Hr16oUpU6Zg/PjxaNy4MU6dOoW5c+eWOH4HBwds2rQJP/74Izw9PbF48WKlQqU62rdvjx9//BG//PILGjdujA4dOiiMYtu4cSOGDRuGqVOnok6dOujZsyfOnj0LV1fXEl+LyiexkQi1bAUENnSGbw17TtkjIiL6z5IlS7B161acPXsWAQEBqF+/PkJDQ9GwYUMMHz4cALB27Vrk5eVh3LhxcHZ2lj8mTZokP4+fnx+ioqKwceNGNGzYEJs2bcL27duLveFPWRAJ6iwiUo6kp6fD1tYWaWlp8v8gKCu5ubmIjo5GonltTPYv/S/rVLzCPHfr1q3Edxkg9RhSjrOyspCYmAgPDw9IXh9eq+dkMhnS09NhY2MDo9fnoNNbuXfvHjw8PBAXF4fq1aszxxqm73/Lxf17QpP9gneRpvJZJt9Lz5+/vMNSRsZbTaMorwzp+9+QMc+axxxrhyHl2VD/m+Fd6GPp37siIiIiIiIiIqJyj0UpIiIiIiIiIiLSOi50TkRE5Y67uzsEQZAPeSYiIiIiIv3DohQRERERvRssLHQdAREREb2CRSkiIiIiKv8sLQsWOyciIiK9wTWliKhceMduJEpEJcB/PxARERHAPkFZK4t8sihFRAZNLBYDAHJycnQcCRHpq8zMTADQ+9tVExERkWYU9gEK+wRUNsqij8Xpe0Rk0IyNjWFhYYF///0XJiYmMDIynFq7TCZDTk4OsrKyDCpuQ8Ica4e+5lkQBGRmZiIlJQUVKlSQF7HpHZWVBfzf/xU837kTkEh0Gw8REWmNWCxGhQoVkJKSAgCwsLCASCTScVRv9i70sViUIiKDJhKJ4OzsjMTERPz111+6DqdEBEHAixcvYG5ubhBfioaIOdYOfc9zhQoV4OTkpOswSNfy84Ho6JfPiYjonVLYFygsTBmCd6GPxaKUhqyI/RNTOtfWdRhE7wRTU1PUqlXL4Kbw5ebm4tixY2jbti2nFWkIc6wd+pxnExMTgxwhtWbNGnz++eeQSqWoX78+Vq5ciTZt2hTZfsuWLfjss89w69Yt2NraokuXLli6dCns7e3lbXbu3Im5c+fizp07qFGjBhYsWIA+ffq81XWJiIgMReGP2ZUrV0Zubq6uw1HLu9DH0nlRqqSdn6NHjyI0NBTXr1+Hi4sLZsyYgTFjxmgxYiLSR0ZGRpAY2FQMsViMvLw8SCQSvfuSKS+YY+1gnsvW9u3bMXnyZKxZswatWrXC2rVr0bVrV8THx6NatWpK7U+cOIFhw4ZhxYoVCAwMxIMHDzBmzBiMGjUKu3fvBgCcPn0aAwYMwKeffoo+ffpg9+7d6N+/P06cOIEWLVqU6rpERESGSCwWG8wPVu9CH0unkxILOz+zZ8/GpUuX0KZNG3Tt2hVJSUkq2ycmJqJbt25o06YNLl26hI8//hgTJ07Ezp07tRw5ERERkWYsX74cI0eOxKhRo1CvXj2sXLkSrq6uiIyMVNn+zJkzcHd3x8SJE+Hh4YHWrVvjww8/xIULF+RtVq5cic6dO2PWrFmoW7cuZs2ahY4dO2LlypWlvi4RERHR29LpSKlXOz9AQYdp//79iIyMxKJFi5Taf/3116hWrZq8A1WvXj1cuHABS5cuxf8VLlypZ1bE/qlyO6f2ERER0etycnIQFxeHmTNnKmz39/fHqVOnVB7j5+eH2bNnIzo6Gl27dkVKSgp++ukndO/eXd7m9OnTmDJlisJxAQEB8j5Vaa4LANnZ2cjOzpa/Tk9PB1Aw3aAsp0YUnuutzpmbCxP501zAQKZuaFOZ5JneiHnWPOZYO5hnzTPkHKsbs86KUqXp/Jw+fRr+/v4K2wICArB+/Xrk5uYa1HC2V9ecKqpwBbB4RURE9C559OgR8vPz4ejoqLDd0dERycnJKo/x8/PDli1bMGDAAGRlZSEvLw89e/bE6tWr5W2Sk5OLPWdprgsAixYtQkREhNL2AwcOwMLCovg3WwqxsbGlPlaclYUe/z3fv38/8g1syrc2vU2eSX3Ms+Yxx9rBPGueIeY4MzNTrXY6K0qVpvNTVIcqLy8Pjx49grOzs9Ixr/+Cl5aWBgB4/PhxmVcbc3NzkZmZiez8NAgiMVJTU5GdkVZk+9TU1IIY39Bm3bG7Re4f3bY6ALyxzducQ91rvN6mcFtZK8xzamqqQRUiDQlzrB3Ms+Yxx9phyHl+9uwZgIK72+iT1++wIwhCkXfdiY+Px8SJE/HJJ58gICAAUqkU06dPx5gxY7B+/foSnbMk1wWAWbNmITQ0VP46LS0N1apVg6+vL6ytrYt/kyWQm5uLw4cP47333iv939jz50j/76mvry9gaVlm8ZUXZZJneiPmWfOYY+1gnjXPkHOsbh9L5wudl7Tzo6q9qu2FivoFz8PDo6Shltist9yvL+co7TXUOY6IiEgfPHv2DLa2troOA5UqVYJYLFb6gS4lJUXph7lCixYtQqtWrTB9+nQAQMOGDWFpaYk2bdpg/vz5cHZ2hpOTU7HnLM11AcDMzAxmZmby14XT97TRz3orbm66joCIiOid8KY+ls6KUqXp/BTVoTI2Nla45fGrXv8FTyaT4fHjx7C3ty+2+FUa6enpcHV1xf3792FjY1Om56aXmGfNY461g3nWPOZYOww5z4Ig4NmzZ3BxcdF1KAAAU1NTNG3aFLGxsejTp498e2xsLHr16qXymMzMTBgbK3bpCu8qVPjjna+vL2JjYxXWlTpw4AD8/PxKfV1VXFxccP/+fVhbW5dpP8uQ/8YMCfOsHcyz5jHH2sE8a54h51jdPpbOilKl6fz4+vpi7969CtsOHDgAHx+fIoeyvf4LHgBUqFDh7YJ/AxsbG4P7gzFEzLPmMcfawTxrHnOsHYaaZ30YIfWq0NBQDB06FD4+PvD19cU333yDpKQkjBkzBkDBD24PHjzA999/DwAIDAzE6NGjERkZKZ++N3nyZDRv3lzeEZw0aRLatm2LJUuWoFevXvj5559x8OBBnDhxQu3rqsPIyAhVq1Ytw2woMtS/MUPDPGsH86x5zLF2MM+aZ6g5VqePpdPpeyXtdI0ZMwZffvklQkNDMXr0aJw+fRrr16/Htm3bdPk2iIiIiMrMgAEDkJqainnz5kEqlcLLywvR0dFw+2/KmVQqRVJSkrx9cHAwnj17hi+//BJTp05FhQoV0KFDByxZskTexs/PD1FRUZgzZw7mzp2LGjVqYPv27WjRooXa1yUiIiIqayJBxyt7rlmzBp999pm887NixQq0bdsWQEEn6969ezhy5Ii8/dGjRzFlyhRcv34dLi4u+Oijj0r0C54mpaenw9bWFmlpaQZZxTQUzLPmMcfawTxrHnOsHcwzaRr/xrSDedYO5lnzmGPtYJ41713Isc4XOg8JCUFISIjKfZs2bVLa1q5dO1y8eFHDUZWOmZkZwsLClKYLUtlinjWPOdYO5lnzmGPtYJ5J0/g3ph3Ms3Ywz5rHHGsH86x570KOdT5SioiIiIiIiIiI3j1Gug6AiIiIiIiIiIjePSxKERERERERERGR1rEoRUREREREREREWseiVBlas2YNPDw8IJFI0LRpUxw/flzXIRmMY8eOITAwEC4uLhCJRNizZ4/CfkEQEB4eDhcXF5ibm6N9+/a4fv26Qpvs7GxMmDABlSpVgqWlJXr27Im///5bi+9Cvy1atAjNmjWDtbU1KleujN69e+PmzZsKbZjntxMZGYmGDRvCxsYGNjY28PX1xW+//Sbfz/yWvUWLFkEkEmHy5Mnybczz2wsPD4dIJFJ4ODk5yfczx6Rt7GOVHvtYmsc+lnawn6V97GdpBvtZrxGoTERFRQkmJibCunXrhPj4eGHSpEmCpaWl8Ndff+k6NIMQHR0tzJ49W9i5c6cAQNi9e7fC/sWLFwvW1tbCzp07hatXrwoDBgwQnJ2dhfT0dHmbMWPGCFWqVBFiY2OFixcvCu+9957QqFEjIS8vT8vvRj8FBAQIGzduFK5duyZcvnxZ6N69u1CtWjUhIyND3oZ5fju//PKLsG/fPuHmzZvCzZs3hY8//lgwMTERrl27JggC81vWzp07J7i7uwsNGzYUJk2aJN/OPL+9sLAwoX79+oJUKpU/UlJS5PuZY9Im9rHeDvtYmsc+lnawn6Vd7GdpDvtZiliUKiPNmzcXxowZo7Ctbt26wsyZM3UUkeF6vcMkk8kEJycnYfHixfJtWVlZgq2trfD1118LgiAIT58+FUxMTISoqCh5mwcPHghGRkZCTEyM1mI3JCkpKQIA4ejRo4IgMM+aUrFiReHbb79lfsvYs2fPhFq1agmxsbFCu3bt5J0l5rlshIWFCY0aNVK5jzkmbWMfq+ywj6Ud7GNpD/tZmsF+lmaxn6WI0/fKQE5ODuLi4uDv76+w3d/fH6dOndJRVOVHYmIikpOTFfJrZmaGdu3ayfMbFxeH3NxchTYuLi7w8vLiZ1CEtLQ0AICdnR0A5rms5efnIyoqCs+fP4evry/zW8bGjRuH7t27o1OnTgrbmeeyc+vWLbi4uMDDwwMDBw7E3bt3ATDHpF3sY2kW/3nWDPaxNI/9LM1iP0vz2M96yVjXAZQHjx49Qn5+PhwdHRW2Ozo6Ijk5WUdRlR+FOVSV37/++kvextTUFBUrVlRqw89AmSAICA0NRevWreHl5QWAeS4rV69eha+vL7KysmBlZYXdu3fD09NT/gXB/L69qKgoXLx4EefPn1fax7/jstGiRQt8//33qF27Nh4+fIj58+fDz88P169fZ45Jq9jH0iz+81z22MfSLPazNI/9LM1jP0sRi1JlSCQSKbwWBEFpG5VeafLLz0C18ePH448//sCJEyeU9jHPb6dOnTq4fPkynj59ip07d2L48OE4evSofD/z+3bu37+PSZMm4cCBA5BIJEW2Y57fTteuXeXPGzRoAF9fX9SoUQPfffcdWrZsCYA5Ju1iH0uz+M9z2WEfS7PYz9Is9rO0g/0sRZy+VwYqVaoEsVisVJVMSUlRqnBSyRXeiaC4/Do5OSEnJwdPnjwpsg0VmDBhAn755RccPnwYVatWlW9nnsuGqakpatasCR8fHyxatAiNGjXCqlWrmN8yEhcXh5SUFDRt2hTGxsYwNjbG0aNH8cUXX8DY2FieJ+a5bFlaWqJBgwa4desW/5ZJq9jH0iz+81y22MfSPPazNIv9LN141/tZLEqVAVNTUzRt2hSxsbEK22NjY+Hn56ejqMoPDw8PODk5KeQ3JycHR48elee3adOmMDExUWgjlUpx7do1fgb/EQQB48ePx65du/D777/Dw8NDYT/zrBmCICA7O5v5LSMdO3bE1atXcfnyZfnDx8cHQUFBuHz5MqpXr848a0B2djYSEhLg7OzMv2XSKvaxNIv/PJcN9rF0h/2sssV+lm688/0s7aynXv4V3q54/fr1Qnx8vDB58mTB0tJSuHfvnq5DMwjPnj0TLl26JFy6dEkAICxfvly4dOmS/HbPixcvFmxtbYVdu3YJV69eFQYNGqTytphVq1YVDh48KFy8eFHo0KGDwd4WUxPGjh0r2NraCkeOHFG4/WhmZqa8DfP8dmbNmiUcO3ZMSExMFP744w/h448/FoyMjIQDBw4IgsD8asqrd4URBOa5LEydOlU4cuSIcPfuXeHMmTNCjx49BGtra/l3GnNM2sQ+1tthH0vz2MfSDvazdIP9rLLHfpYiFqXK0FdffSW4ubkJpqamgre3t/w2sPRmhw8fFgAoPYYPHy4IQsGtMcPCwgQnJyfBzMxMaNu2rXD16lWFc7x48UIYP368YGdnJ5ibmws9evQQkpKSdPBu9JOq/AIQNm7cKG/DPL+dESNGyP8d4ODgIHTs2FHeURIE5ldTXu8sMc9vb8CAAYKzs7NgYmIiuLi4CH379hWuX78u388ck7axj1V67GNpHvtY2sF+lm6wn1X22M9SJBIEQdDeuCwiIiIiIiIiIiKuKUVERERERERERDrAohQREREREREREWkdi1JERERERERERKR1LEoREREREREREZHWsShFRERERERERERax6IUERERERERERFpHYtSRERERERERESkdSxKERERERERERGR1rEoRUQqtW/fHpMnTy6z84WHh6Nx48Zldj4AuHfvHkQiES5fvlym5yUiIqJ3lz72L27cuIGWLVtCIpGUeX+KfT4i0iUWpYjKueDgYIhEIohEIpiYmKB69eqYNm0anj9/Xuxxu3btwqefflpmcUybNg2HDh0qs/OVxO3bt/HBBx+gatWqMDMzg4eHBwYNGoQLFy7oJB59VdadUiIiotIo7LssXrxYYfuePXsgEol0FJVuhYWFwdLSEjdv3iyyP8U+H/t86mKfj/QJi1JE74AuXbpAKpXi7t27mD9/PtasWYNp06apbJubmwsAsLOzg7W1dZnFYGVlBXt7+zI7n7ouXLiApk2b4s8//8TatWsRHx+P3bt3o27dupg6darW4yEiIqI3k0gkWLJkCZ48eaLrUMpMTk5OqY+9c+cOWrduDTc3t2L7U+zzsc9HZGhYlCJ6B5iZmcHJyQmurq4YPHgwgoKCsGfPHgAvh1hv2LAB1atXh5mZGQRBUPoFxd3dHQsXLsSIESNgbW2NatWq4ZtvvlG4zt9//42BAwfCzs4OlpaW8PHxwdmzZxWuUyg4OBi9e/dGREQEKleuDBsbG3z44YcKHbaYmBi0bt0aFSpUgL29PXr06IE7d+6o/b4FQUBwcDBq1aqF48ePo3v37qhRowYaN26MsLAw/Pzzz/K2V69eRYcOHWBubg57e3v873//Q0ZGhlK8CxcuhKOjIypUqICIiAjk5eVh+vTpsLOzQ9WqVbFhwwb5MYVDzaOiouDn5weJRIL69evjyJEjCnEePXoUzZs3h5mZGZydnTFz5kzk5eXJ97dv3x4TJ07EjBkzYGdnBycnJ4SHhyucIy0tDf/73//kuezQoQOuXLki31+Y/x9++AHu7u6wtbXFwIED8ezZM/n7O3r0KFatWiX/lfXevXtq55qIiKgsderUCU5OTli0aFGRbVRNE1u5ciXc3d3lr0vz/V3oxo0bxX5/x8fHo1u3brCysoKjoyOGDh2KR48eyfe3b98e48ePR2hoKCpVqoTOnTurfB8ymQzz5s2Tj+5p3LgxYmJi5PtFIhHi4uIwb948iEQipT7Aq9jnY5+vtH2+J0+eICgoCA4ODjA3N0etWrWwceNGtT8DotJiUYroHWRubi7/dQwoGOq8Y8cO7Ny5s9i5+suWLYOPjw8uXbqEkJAQjB07Fjdu3AAAZGRkoF27dvjnn3/wyy+/4MqVK5gxYwZkMlmR5zt06BASEhJw+PBhbNu2Dbt370ZERIR8//PnzxEaGorz58/j0KFDMDIyQp8+fYo956suX76M69evY+rUqTAyUv7XXYUKFQAAmZmZ6NKlCypWrIjz58/jxx9/xMGDBzF+/HiF9r///jv++ecfHDt2DMuXL0d4eDh69OiBihUr4uzZsxgzZgzGjBmD+/fvKxw3ffp0TJ06FZcuXYKfnx969uyJ1NRUAMCDBw/QrVs3NGvWDFeuXEFkZCTWr1+P+fPnK5zju+++g6WlJc6ePYvPPvsM8+bNQ2xsLICCjlj37t2RnJyM6OhoxMXFwdvbGx07dsTjx4/l57hz5w727NmDX3/9Fb/++iuOHj0qnxqxatUq+Pr6YvTo0ZBKpZBKpXB1dVUrz0RERGVNLBZj4cKFWL16Nf7++++3Opcmvr+lUinatWuHxo0b48KFC4iJicHDhw/Rv39/hXN89913MDY2xsmTJ7F27VqV8a1atQrLli3D0qVL8ccffyAgIAA9e/bErVu35NeqX78+pk6dCqlUWuTIJ1XY5yvAPt+b+3xz585FfHw8fvvtNyQkJCAyMhKVKlVSK/9Eb0UgonJt+PDhQq9eveSvz549K9jb2wv9+/cXBEEQwsLCBBMTEyElJUXhuHbt2gmTJk2Sv3ZzcxOGDBkify2TyYTKlSsLkZGRgiAIwtq1awVra2shNTVVZRxhYWFCo0aNFOKys7MTnj9/Lt8WGRkpWFlZCfn5+SrPkZKSIgAQrl69KgiCICQmJgoAhEuXLqlsv337dgGAcPHiRZX7C33zzTdCxYoVhYyMDPm2ffv2CUZGRkJycrI8Xjc3N4XY6tSpI7Rp00b+Oi8vT7C0tBS2bdumEN/ixYvlbXJzc4WqVasKS5YsEQRBED7++GOhTp06gkwmk7f56quvFPLQrl07oXXr1goxN2vWTPjoo48EQRCEQ4cOCTY2NkJWVpZCmxo1aghr164VBKEg/xYWFkJ6erp8//Tp04UWLVrIX7/+mRMREenCq32Xli1bCiNGjBAEQRB2794tvPqfL6/3LQRBEFasWCG4ubkpnEsT399z584V/P39Fa59//59AYBw8+ZNQRAKvlcbN278xvfr4uIiLFiwQGFbs2bNhJCQEPnrRo0aCWFhYcWeh30+9vkEofR9vsDAQOGDDz4oNn9EmsCRUkTvgF9//RVWVlaQSCTw9fVF27ZtsXr1avl+Nzc3ODg4vPE8DRs2lD8XiURwcnJCSkoKgIJfqJo0aQI7Ozu142rUqBEsLCzkr319fZGRkSH/1enOnTsYPHgwqlevDhsbG3h4eAAAkpKS1Dq/IAjyWIuTkJCARo0awdLSUr6tVatWkMlkuHnzpnxb/fr1FX59c3R0RIMGDeSvxWIx7O3t5Tl59X0VMjY2ho+PDxISEuTX9vX1VYixVatWyMjIUPhl+NXcA4Czs7P8OnFxccjIyIC9vT2srKzkj8TERIWh7+7u7gprRrx6DiIiIn20ZMkSfPfdd4iPjy/1OTTx/R0XF4fDhw8rfO/WrVsXABS+e318fIqNLT09Hf/88w9atWqlsL1Vq1bya5UE+3zs8wGl6/ONHTsWUVFRaNy4MWbMmIFTp04V256orBjrOgAi0rz33nsPkZGRMDExgYuLC0xMTBT2v/rFXJzXjxOJRPJh1ebm5mUTLF52KAIDA+Hq6op169bBxcUFMpkMXl5eai8UWrt2bQAFnYDibk0sCEKRnZhXt6t6/8XlpDiF51V1bVUdq+KuI5PJ4OzsrLRuAfByuPqbzkFERKSP2rZti4CAAHz88ccIDg5W2GdkZCT/ziz06lS1Qpr4/pbJZAgMDMSSJUuU2jg7O8ufq9vHUtUXKM2dBtnnY5/vTecoSteuXfHXX39h3759OHjwIDp27Ihx48Zh6dKlxb9BorfEkVJE7wBLS0vUrFkTbm5uSl9SZaVhw4a4fPmywnz2N7ly5QpevHghf33mzBlYWVmhatWqSE1NRUJCAubMmYOOHTuiXr16Jb4DT+PGjeHp6Ylly5ap/CJ++vQpAMDT0xOXL19WuGXyyZMnYWRkJO/kvI0zZ87In+fl5SEuLk7+a6qnpydOnTql0Kk+deoUrK2tUaVKFbXO7+3tjeTkZBgbG6NmzZoKj5KsBWBqaor8/Hy12xMREWnD4sWLsXfvXqWRGw4ODkhOTlb4Di1unaSSKu7729vbG9evX4e7u7vSd6+6hR8AsLGxgYuLC06cOKGw/dSpU6hXr16JY2afj30+dRTV53NwcEBwcDA2b96MlStXKi1wT6QJLEoRUZkYNGgQnJyc0Lt3b5w8eRJ3797Fzp07cfr06SKPycnJwciRI+WLKoaFhWH8+PEwMjJCxYoVYW9vj2+++Qa3b9/G77//jtDQ0BLFJBKJsHHjRvz5559o27YtoqOjcffuXfzxxx9YsGABevXqBQAICgqCRCLB8OHDce3aNRw+fBgTJkzA0KFD4ejo+FZ5AYCvvvoKu3fvxo0bNzBu3Dg8efIEI0aMAACEhITg/v37mDBhAm7cuIGff/4ZYWFhCA0NVblQpyqdOnWCr68vevfujf379+PevXs4deoU5syZgwsXLqgdp7u7O86ePYt79+7h0aNHHEVFRER6oUGDBggKClKYhgYU3Kns33//xWeffYY7d+7gq6++wm+//VZm1y3u+3vcuHF4/PgxBg0ahHPnzuHu3bs4cOAARowYUeIfeKZPn44lS5Zg+/btuHnzJmbOnInLly9j0qRJZfZeyhL7fEUz5D7fJ598gp9//hm3b9/G9evX8euvv5aqMEpUUixKEVGZMDU1xYEDB1C5cmV069YNDRo0wOLFiyEWi4s8pmPHjqhVqxbatm2L/v37IzAwUH7bWyMjI0RFRSEuLg5eXl6YMmUKPv/88xLH1bx5c1y4cAE1atTA6NGjUa9ePfTs2RPXr1/HypUrAQAWFhbYv38/Hj9+jGbNmqFfv37o2LEjvvzyy9KkQsnixYuxZMkSNGrUCMePH8fPP/8s/zWrSpUqiI6Oxrlz59CoUSOMGTMGI0eOxJw5c9Q+v0gkQnR0NNq2bYsRI0agdu3aGDhwIO7du1eiDta0adMgFovh6ekJBwcHtddxICIi0rRPP/1UaapevXr1sGbNGnz11Vdo1KgRzp07V6I7071Jcd/fLi4uOHnyJPLz8xEQEAAvLy9MmjQJtra2ahcYCk2cOBFTp07F1KlT0aBBA8TExOCXX35BrVq1yuy9lCX2+YpmyH0+U1NTzJo1Cw0bNkTbtm0hFosRFRVV4hwQlZRIeP3f7kREWhAcHIynT59iz549ug5FY+7duwcPDw9cunSp2PUNiIiIiMor9vmIqDgcKUVERERERERERFrHohQREREREREREWkdp+8REREREREREZHWcaQUERERERERERFpHYtSRERERERERESkdSxKERERERERERGR1rEoRUREREREREREWseiFBERERERERERaR2LUkREREREREREpHUsShERERERERERkdaxKEVERERERERERFrHohQREREREREREWnd/wMFk7fMNflf7gAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 1200x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Summary of Explained Variance:\n",
      "  - The first 1 PCs explain 80.89% of the variance.\n",
      "  - The first 2 PCs explain 82.11% of the variance.\n",
      "  - The first 5 PCs explain 84.00% of the variance.\n",
      "  - The first 10 PCs explain 86.56% of the variance.\n",
      "  - The first 20 PCs explain 90.21% of the variance.\n",
      "  - The first 30 PCs explain 92.52% of the variance.\n"
     ]
    }
   ],
   "source": [
    "# -------------------------------\n",
    "# Perform PCA with full components to get explained variance\n",
    "# -------------------------------\n",
    "pca = PCA(n_components=None)\n",
    "pca.fit(X)\n",
    "\n",
    "# Get explained variance ratio for each component\n",
    "explained_variance = pca.explained_variance_ratio_\n",
    "\n",
    "# Calculate cumulative explained variance\n",
    "cumulative_explained_variance = np.cumsum(explained_variance)\n",
    "\n",
    "# -------------------------------\n",
    "# Plot the results\n",
    "# -------------------------------\n",
    "plt.figure(figsize=(12, 6))\n",
    "\n",
    "# Plot explained variance per PC (Scree Plot)\n",
    "plt.subplot(1, 2, 1)\n",
    "plt.bar(range(1, len(explained_variance) + 1), explained_variance, alpha=0.5, align='center', label='Individual explained variance')\n",
    "plt.step(range(1, len(explained_variance) + 1), cumulative_explained_variance, where='mid', label='Cumulative explained variance')\n",
    "plt.ylabel('Explained variance ratio')\n",
    "plt.xlabel('Principal Component')\n",
    "plt.title('Explained Variance by PC')\n",
    "plt.legend(loc='best')\n",
    "plt.grid(True)\n",
    "\n",
    "# Plot cumulative explained variance with a focus on first 20 PCs\n",
    "plt.subplot(1, 2, 2)\n",
    "plt.plot(range(1, len(cumulative_explained_variance) + 1), cumulative_explained_variance, marker='o', linestyle='-')\n",
    "plt.xlabel(\"Number of Principal Components\")\n",
    "plt.ylabel(\"Cumulative Explained Variance\")\n",
    "plt.title(\"Cumulative Explained Variance\")\n",
    "plt.axvline(x=20, color='r', linestyle='--', label='PC20')\n",
    "plt.grid(True)\n",
    "plt.legend()\n",
    "plt.tight_layout()\n",
    "\n",
    "# Display the plot\n",
    "plt.show()\n",
    "\n",
    "# Print a summary of variance explained by key PCs\n",
    "print(\"\\nSummary of Explained Variance:\")\n",
    "for n in [1, 2, 5, 10, 20, 30]:\n",
    "    if n <= len(cumulative_explained_variance):\n",
    "        print(f\"  - The first {n} PCs explain {cumulative_explained_variance[n-1]:.2%} of the variance.\")\n",
    "    else:\n",
    "        break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "4f992d3a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "✅ Absolute loadings, per-PC contribution scores, cumulative scores, and top 20 features saved in 'PCA_feature_contributions_sep30.xlsx'\n"
     ]
    }
   ],
   "source": [
    "# -------------------------------\n",
    "# Step 2: Find Top 20 Features by PCA Contribution\n",
    "# -------------------------------\n",
    "n_components_for_pca = 20\n",
    "pca = PCA(n_components=n_components_for_pca)\n",
    "pca.fit(X_scaled)\n",
    "    \n",
    "# Create a DataFrame of the loadings\n",
    "loadings = pd.DataFrame(\n",
    "pca.components_.T,\n",
    "columns=[f'PC{i+1}' for i in range(n_components_for_pca)],\n",
    "index=X_scaled.columns\n",
    "    )\n",
    "    \n",
    "# Absolute contribution matrix (|loadings|)\n",
    "abs_contrib = loadings.abs()\n",
    "\n",
    "# Individual contribution scores (normalize per PC so each PC sums to 1)\n",
    "contrib_ratios = abs_contrib.div(abs_contrib.sum(axis=0), axis=1)\n",
    "\n",
    "# Final cumulative contribution score for each feature (sum across PCs)\n",
    "cumulative_contrib = contrib_ratios.sum(axis=1)\n",
    "\n",
    "# Top 20 features by cumulative contribution\n",
    "top_20_features = cumulative_contrib.sort_values(ascending=False).head(20).index.tolist()\n",
    "\n",
    "# -------------------------------\n",
    "# Save to Excel\n",
    "# -------------------------------\n",
    "with pd.ExcelWriter(\"PCA_feature_contributions_sep30_final.xlsx\") as writer:\n",
    "    # Absolute loadings per PC\n",
    "    abs_contrib.to_excel(writer, sheet_name=\"Absolute_Loadings\")\n",
    "    \n",
    "    # Individual contribution scores per feature per PC\n",
    "    contrib_ratios.to_excel(writer, sheet_name=\"PerPC_Contributions\")\n",
    "    \n",
    "    # Contribution score of all features (summed across PCs)\n",
    "    cumulative_contrib.to_excel(writer, sheet_name=\"All_Features\", header=[\"CumulativeContributionScore\"])\n",
    "    \n",
    "    # Top 20 features by contribution\n",
    "    pd.DataFrame(top_20_features).to_excel(writer, sheet_name=\"Top20_Features\", header=[\"CumulativeContributionScore\"])\n",
    "\n",
    "print(\"✅ Absolute loadings, per-PC contribution scores, cumulative scores, and top 20 features saved in 'PCA_feature_contributions_sep30.xlsx'\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "a8a187fd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "✅ Top 20 features identified:\n",
      "['A:245-phi', 'A:251-psi', 'A:245-psi', 'A:181-phi', 'A:251-phi', 'A:116-phi', 'A:238-phi', 'A:196-psi', 'A:144-phi', 'A:144-psi', 'A:42-phi', 'A:196-phi', 'A:156-psi', 'A:156-phi', 'A:238-psi', 'A:254-phi', 'A:227-phi', 'A:254-psi', 'A:105-phi', 'A:228-psi']\n"
     ]
    }
   ],
   "source": [
    "print(\"✅ Top 20 features identified:\")\n",
    "print(top_20_features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "bab47b3b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "✅ Data filtered to include only the top 20 features.\n"
     ]
    }
   ],
   "source": [
    "X_selected = X_scaled[top_20_features]\n",
    "print(\"✅ Data filtered to include only the top 20 features.\")\n",
    "y = data['GROUP']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "e818d4c2",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(\n",
    "    X_selected, y, test_size=0.2, random_state=42, stratify=y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "db7f4b2d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-1 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-1 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-1 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-1 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-1 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier(max_depth=10, n_estimators=10, random_state=42)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;RandomForestClassifier<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.ensemble.RandomForestClassifier.html\">?<span>Documentation for RandomForestClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>RandomForestClassifier(max_depth=10, n_estimators=10, random_state=42)</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "RandomForestClassifier(max_depth=10, n_estimators=10, random_state=42)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# -------------------------------\n",
    "# Random Forest Classifier\n",
    "# -------------------------------\n",
    "rf_model = RandomForestClassifier(\n",
    "    n_estimators=10,\n",
    "    max_depth=10,\n",
    "    random_state=42\n",
    ")\n",
    "\n",
    "rf_model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "ba91d1c8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Predictions\n",
    "y_train_pred = rf_model.predict(X_train)\n",
    "y_test_pred = rf_model.predict(X_test)\n",
    "y_proba = rf_model.predict_proba(X_test)[:, 1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "f7654dc3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Accuracy: 0.9049444444444444\n",
      "Testing Accuracy: 0.8977777777777778\n",
      "MCC: 0.7983472798043758\n",
      "\n",
      "Classification Report:\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.87      0.94      0.90     18000\n",
      "           1       0.93      0.86      0.89     18000\n",
      "\n",
      "    accuracy                           0.90     36000\n",
      "   macro avg       0.90      0.90      0.90     36000\n",
      "weighted avg       0.90      0.90      0.90     36000\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "✅ ROC curve saved as 'random_forest_roc.png'.\n"
     ]
    }
   ],
   "source": [
    "# Metrics\n",
    "print(\"Training Accuracy:\", accuracy_score(y_train, y_train_pred))\n",
    "print(\"Testing Accuracy:\", accuracy_score(y_test, y_test_pred))\n",
    "print(\"MCC:\", matthews_corrcoef(y_test, y_test_pred))\n",
    "print(\"\\nClassification Report:\\n\", classification_report(y_test, y_test_pred))\n",
    "\n",
    "# -------------------------------\n",
    "# ROC Curve\n",
    "# -------------------------------\n",
    "fpr, tpr, _ = roc_curve(y_test, y_proba)\n",
    "roc_auc = auc(fpr, tpr)\n",
    "\n",
    "plt.figure(figsize=(8, 6))\n",
    "plt.plot(fpr, tpr, lw=2, label=f\"Random Forest (AUC = {roc_auc:.2f})\")\n",
    "plt.plot([0, 1], [0, 1], color=\"gray\", lw=2, linestyle=\"--\")\n",
    "plt.xlabel(\"False Positive Rate\")\n",
    "plt.ylabel(\"True Positive Rate\")\n",
    "plt.title(\"ROC Curve - Random Forest\")\n",
    "plt.legend(loc=\"lower right\")\n",
    "plt.grid(True)\n",
    "plt.tight_layout()\n",
    "plt.savefig(\"random_forest_roc.png\", dpi=300)\n",
    "plt.show()\n",
    "\n",
    "print(\"✅ ROC curve saved as 'random_forest_roc.png'.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c83f0dbe",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
